Amgen

Senior Data Engineer

India - Hyderabad

Pay: Pay not listed
Work setting: Not stated
Employment: Full-time — employment source
Position Type Full time .
Employment type in the employer’s posting: Full time
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What you’ll work on

Full posting

Amgen’s Data Platform team is seeking a senior, hands-on Databricks Subject Matter Expert to lead the design, evaluation, engineering, governance, and enterprise enablement of capabilities on the Databricks Data Intelligence Platform.

The Data Platform team owns the enterprise platform architecture, governance model, policies, engineering standards, guardrails, lifecycle, observability, cost management, security controls, feature evaluations, and reusable platform services that enable data engineering, analytics, BI, data science, machine learning, and generative AI teams.

Shape the Databricks platform roadmap, reference architecture and standards
Evaluate Databricks features and third-party tools through assessments and PoCs
Build reusable frameworks, templates and automation using Terraform and Databricks SDKs
Define and enforce security guardrails and Unity Catalog access controls
Lead FinOps work on tagging, budgets, chargeback and compute policies

From the employer’s posting

Amgen’s Data Platform team is seeking a senior, hands-on Databricks Subject Matter Expert to lead the design, evaluation, engineering, governance, and enterprise enablement of capabilities on the Databricks Data Intelligence Platform.

The Data Platform team owns the enterprise platform architecture, governance model, policies, engineering standards, guardrails, lifecycle, observability, cost management, security controls, feature evaluations, and reusable platform services that enable data engineering, analytics, BI, data science, machine learning, and generative AI teams.

Serve as the Databricks platform lead, shaping the platform roadmap, reference architecture, standards, operating model, guardrails, lifecycle strategy, and capability backlog

Evaluate Databricks features and third-party technologies through structured assessments and PoCs.

Build reusable frameworks, accelerators, platform APIs, custom templates, provisioning workflows, policy-as-code controls, and reference implementations using Terraform, Databricks SDKs and REST APIs

Define and enforce security guardrails covering identity federation, SSO/SCIM, OAuth, service principals, least privilege, segregation of duties, Unity Catalog access controls and ABAC

Lead Databricks FinOps, including tagging, allocation, showback or chargeback, budgets, alerts, compute policies

What you’ll bring

All qualifications

Core experience

Master’s or Bachelor’s degree in computer science or engineering field and 9 to12 years of relevant experience
Demonstrated experience owning or technically leading an enterprise Databricks platform, extending beyond notebook or pipeline development into architecture, administration, governance, security, lifecycle management, reliability, and enablement.
Experience with current Databricks agent capabilities, including Agent Bricks, custom agents, Knowledge Assistant, Supervisor Agent, AI Playground, tool calling, multi-agent systems, and Model Context Protocol integrations.
Ability to balance innovation with enterprise risk, distinguishing between experimentation, limited preview adoption, and production-ready capabilities.
Proven ability to design enterprise account, workspace, metastore, catalog, dev/test/prod, workload-isolation, promotion, high-availability, disaster-recovery, and capacity strategies, and to establish enforceable platform standards and lifecycle controls.
Experience building governed RAG solutions with Databricks AI Search-formerly Vector Search-and MLflow for GenAI, including embeddings, hybrid retrieval, reranking, tracing, evaluation datasets, LLM judges, human feedback, production monitoring, and quality, latency, and cost analysis.

Qualification wording

Master’s or Bachelor’s degree in computer science or engineering field and 9 to12 years of relevant experience

Demonstrated experience owning or technically leading an enterprise Databricks platform, extending beyond notebook or pipeline development into architecture, administration, governance, security, lifecycle management, reliability, and enablement.

Experience with current Databricks agent capabilities, including Agent Bricks, custom agents, Knowledge Assistant, Supervisor Agent, AI Playground, tool calling, multi-agent systems, and Model Context Protocol integrations.

Ability to balance innovation with enterprise risk, distinguishing between experimentation, limited preview adoption, and production-ready capabilities.

Proven ability to design enterprise account, workspace, metastore, catalog, dev/test/prod, workload-isolation, promotion, high-availability, disaster-recovery, and capacity strategies, and to establish enforceable platform standards and lifecycle controls.

Experience building governed RAG solutions with Databricks AI Search-formerly Vector Search-and MLflow for GenAI, including embeddings, hybrid retrieval, reranking, tracing, evaluation datasets, LLM judges, human feedback, production monitoring, and quality, latency, and cost analysis.

Education & alternatives

Basic Qualifications and Experience: - Master’s or Bachelor’s degree in computer science or engineering field and 9 to12 years of relevant experience Must-Have Skills:

Tools in this posting

Python
SQL
AWS
Azure
Databricks
Datadog
Delta Lake
Docker
GCP
Iceberg
Kubernetes
MLflow
NoSQL
S3
Spark
Terraform
PySpark
PostgreSQL

Source

- Strong understanding of Databricks AI/ML foundations, including MLflow, Models in Unity Catalog, feature engineering or Feature Store, batch and real-time inference, Model Serving, inference tables, model monitoring, MLOps, RAG, agent evaluation, AI security, and LLM cost and performance considerations. - Strong Python, PySpark, SQL, automation, and platform API skills. Hands-on experience with Databricks SDKs, REST APIs, CLI, Terraform, Git, CI/CD, automated testing, controlled promotion, rollback, secrets management, and Declarative Automation Bundles or equivalent deployment patterns. - Strong Spark and SQL troubleshooting and performance-analysis skills, including query plans, partitioning, shuffles, skew, file sizing, table optimization, concurrency, cluster utilization, and Photon-enabled workloads.

- Hands-on experience integrating Databricks with Collibra and working with data-quality or observability products such as Ataccama, Monte Carlo, Datadog, or Splunk. - Experience developing self-service portals, Python microservices, and secure platform APIs, including collaboration with React teams and deployment using Docker, Kubernetes, or Amazon EKS. - Experience with Lakehouse Federation, Delta Sharing or OpenSharing, Clean Rooms, Databricks Marketplace, Apache Iceberg interoperability, external lineage, SQL/NoSQL/vector databases, Lakebase/Postgres, or Azure and GCP Databricks environments.

- Evaluate Databricks features and third-party technologies through structured assessments and PoCs. Assess architectural fit, security, privacy, compliance, interoperability, performance, reliability, cost, supportability, and user experience before recommending adoption, restriction, deferral, or rejection. - Lead architecture reviews covering account, workspace, metastore, catalog, networking, storage, compute, SQL, ML/AI, dev/test/prod isolation, regional deployment, serverless and classic compute, capacity, upgrades, high availability, disaster recovery, and RTO/RPO requirements. - Build reusable frameworks, accelerators, platform APIs, custom templates, provisioning workflows, policy-as-code controls, and reference implementations using Terraform, Databricks SDKs and REST APIs, the Databricks CLI, and Declarative Automation Bundles-formerly Databricks Asset Bundles.

- Lead Databricks FinOps, including tagging, allocation, showback or chargeback, budgets, alerts, compute policies, serverless usage policies, cost-anomaly detection, and model or token cost controls. Optimize spend through rightsizing, Photon, autoscaling, auto-termination, workload isolation, and appropriate compute selection. - Establish platform observability through SLOs, KPIs, telemetry, dashboards, and alerts for availability, reliability, jobs, queries, compute, SQL warehouses, capacity, security, adoption, and cost, using system tables, audit logs, billing data, lineage, data-quality monitoring, inference tables, and MLflow tracing. - Enable governed ML, GenAI, RAG, and agent capabilities, including feature and model lifecycle, Model Serving, AI Search, foundation-model access, evaluation, monitoring, and LLMOps. Define responsible-AI guardrails for models, agents, prompts, retrieval components, MCP tools, and external providers.

- Demonstrated experience owning or technically leading an enterprise Databricks platform, extending beyond notebook or pipeline development into architecture, administration, governance, security, lifecycle management, reliability, and enablement. - Broad hands-on Databricks knowledge, with expert depth across several areas such as account and workspace administration, serverless and classic compute, Apache Spark, Delta Lake, Unity Catalog, Lakeflow Jobs and Pipelines, Databricks SQL, Photon, AI/BI, MLflow, and Model Serving. - Proven ability to design enterprise account, workspace, metastore, catalog, dev/test/prod, workload-isolation, promotion, high-availability, disaster-recovery, and capacity strategies, and to establish enforceable platform standards and lifecycle controls.

- Strong AWS experience with IAM, VPC, PrivateLink, S3, EC2, KMS, CloudWatch, CloudTrail, Secrets Manager, and STS. Familiarity with EKS, Lambda, Glue, EMR, and RDS is beneficial. - Proven cost-management and observability experience, including compute rightsizing, serverless adoption, SQL warehouse optimization, Photon, budgets, tagging, showback or chargeback, system billing data, system tables, operational telemetry, dashboards, alerts, and cost-anomaly investigation. - Strong understanding of Databricks AI/ML foundations, including MLflow, Models in Unity Catalog, feature engineering or Feature Store, batch and real-time inference, Model Serving, inference tables, model monitoring, MLOps, RAG, agent evaluation, AI security, and LLM cost and performance considerations.

- Strong Python, PySpark, SQL, automation, and platform API skills. Hands-on experience with Databricks SDKs, REST APIs, CLI, Terraform, Git, CI/CD, automated testing, controlled promotion, rollback, secrets management, and Declarative Automation Bundles or equivalent deployment patterns. - Strong Spark and SQL troubleshooting and performance-analysis skills, including query plans, partitioning, shuffles, skew, file sizing, table optimization, concurrency, cluster utilization, and Photon-enabled workloads. - Strong software-engineering and distributed-systems fundamentals, including modular and API design, version control, testing, secure coding, maintainability, production support, technical documentation, relational and dimensional modeling, operational readiness, and recovery patterns.

- Experience with Unity Gateway or AI Gateway, Foundation Model APIs, external models, AI Functions, batch inference, custom Model Serving endpoints, feature serving, provider routing, rate limits, budgets, service policies, guardrails, auditing, and token-cost attribution. - Experience with Databricks AI/BI dashboards, Genie Agents, Genie Code, Databricks Apps, natural-language analytics, semantic metadata, AI-generated SQL controls, and responsible-AI practices such as prompt-injection defense, tool authorization, data-leakage prevention, red teaming, and human oversight. - Hands-on experience integrating Databricks with Collibra and working with data-quality or observability products such as Ataccama, Monte Carlo, Datadog, or Splunk.

- Experience developing self-service portals, Python microservices, and secure platform APIs, including collaboration with React teams and deployment using Docker, Kubernetes, or Amazon EKS. - Experience with Lakehouse Federation, Delta Sharing or OpenSharing, Clean Rooms, Databricks Marketplace, Apache Iceberg interoperability, external lineage, SQL/NoSQL/vector databases, Lakebase/Postgres, or Azure and GCP Databricks environments. - Experience supporting data and AI platforms in life sciences or another regulated industry. Preferred certifications include::- Databricks Certified Data Engineer Professional

The Data Platform team owns the enterprise platform architecture, governance model, policies, engineering standards, guardrails, lifecycle, observability, cost management, security controls, feature evaluations, and reusable platform services that enable data engineering, analytics, BI, data science, machine learning, and generative AI teams. This is a senior individual-contributor and technical-leadership role. The successful candidate will combine deep Databricks platform expertise with strong AWS, security, governance, FinOps, observability, automation, and AI/ML knowledge. The role will translate business and technology needs into secure, scalable, reusable, observable, and cost-efficient platform capabilities. This role enables delivery teams through paved-road patterns, frameworks, APIs, automation, reference implementations, architecture reviews, and expert guidance. It does not own the architecture or implementation of business-specific data pipelines.

- Strong Unity Catalog and security expertise, including privileges, ownership, managed and external storage, governed tags, lineage, workspace bindings, row filters, column masks, ABAC, SSO/SCIM, OAuth, service principals, secrets, encryption, private connectivity, auditing, and data-exfiltration controls. - Strong AWS experience with IAM, VPC, PrivateLink, S3, EC2, KMS, CloudWatch, CloudTrail, Secrets Manager, and STS. Familiarity with EKS, Lambda, Glue, EMR, and RDS is beneficial. - Proven cost-management and observability experience, including compute rightsizing, serverless adoption, SQL warehouse optimization, Photon, budgets, tagging, showback or chargeback, system billing data, system tables, operational telemetry, dashboards, alerts, and cost-anomaly investigation.

- Databricks Certified Generative AI Engineer Associate - AWS Certified Data Engineer - Associate - AWS Certified Solutions Architect

- AWS Certified Data Engineer - Associate - AWS Certified Solutions Architect - AWS Certified Security - Specialty

- AWS Certified Solutions Architect - AWS Certified Security - Specialty - AWS Certified Machine Learning Engineer - Associate

- AWS Certified Security - Specialty - AWS Certified Machine Learning Engineer - Associate - SAFe Agilist or another SAFe certification

Our award-winning culture is collaborative, innovative, and science based. If you have a passion for challenges and the opportunities that lay within them, you’ll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career. Senior Databricks Platform Engineer About Amgen

About the Role Amgen’s Data Platform team is seeking a senior, hands-on Databricks Subject Matter Expert to lead the design, evaluation, engineering, governance, and enterprise enablement of capabilities on the Databricks Data Intelligence Platform. The Data Platform team owns the enterprise platform architecture, governance model, policies, engineering standards, guardrails, lifecycle, observability, cost management, security controls, feature evaluations, and reusable platform services that enable data engineering, analytics, BI, data science, machine learning, and generative AI teams.

Roles & Responsibilities: - Serve as the Databricks platform lead, shaping the platform roadmap, reference architecture, standards, operating model, guardrails, lifecycle strategy, and capability backlog in partnership with Enterprise Data Architecture, security, governance, operations, and delivery teams. - Evaluate Databricks features and third-party technologies through structured assessments and PoCs. Assess architectural fit, security, privacy, compliance, interoperability, performance, reliability, cost, supportability, and user experience before recommending adoption, restriction, deferral, or rejection.

- Serve as the Databricks platform lead, shaping the platform roadmap, reference architecture, standards, operating model, guardrails, lifecycle strategy, and capability backlog in partnership with Enterprise Data Architecture, security, governance, operations, and delivery teams. - Evaluate Databricks features and third-party technologies through structured assessments and PoCs. Assess architectural fit, security, privacy, compliance, interoperability, performance, reliability, cost, supportability, and user experience before recommending adoption, restriction, deferral, or rejection. - Lead architecture reviews covering account, workspace, metastore, catalog, networking, storage, compute, SQL, ML/AI, dev/test/prod isolation, regional deployment, serverless and classic compute, capacity, upgrades, high availability, disaster recovery, and RTO/RPO requirements.

- Lead architecture reviews covering account, workspace, metastore, catalog, networking, storage, compute, SQL, ML/AI, dev/test/prod isolation, regional deployment, serverless and classic compute, capacity, upgrades, high availability, disaster recovery, and RTO/RPO requirements. - Build reusable frameworks, accelerators, platform APIs, custom templates, provisioning workflows, policy-as-code controls, and reference implementations using Terraform, Databricks SDKs and REST APIs, the Databricks CLI, and Declarative Automation Bundles-formerly Databricks Asset Bundles. - Design and operationalize integrations between Databricks and third party services like Collibra, enabling enterprise metadata, classification, ownership, lineage, business glossary, data-quality, AI-asset, and governed-tag capabilities.

- Build reusable frameworks, accelerators, platform APIs, custom templates, provisioning workflows, policy-as-code controls, and reference implementations using Terraform, Databricks SDKs and REST APIs, the Databricks CLI, and Declarative Automation Bundles-formerly Databricks Asset Bundles. - Design and operationalize integrations between Databricks and third party services like Collibra, enabling enterprise metadata, classification, ownership, lineage, business glossary, data-quality, AI-asset, and governed-tag capabilities. - Define and enforce security guardrails covering identity federation, SSO/SCIM, OAuth, service principals, least privilege, segregation of duties, Unity Catalog access controls and ABAC, secrets, encryption, private connectivity, egress controls, auditing, and data-exfiltration prevention.

- Define and enforce security guardrails covering identity federation, SSO/SCIM, OAuth, service principals, least privilege, segregation of duties, Unity Catalog access controls and ABAC, secrets, encryption, private connectivity, egress controls, auditing, and data-exfiltration prevention. - Lead Databricks FinOps, including tagging, allocation, showback or chargeback, budgets, alerts, compute policies, serverless usage policies, cost-anomaly detection, and model or token cost controls. Optimize spend through rightsizing, Photon, autoscaling, auto-termination, workload isolation, and appropriate compute selection. - Establish platform observability through SLOs, KPIs, telemetry, dashboards, and alerts for availability, reliability, jobs, queries, compute, SQL warehouses, capacity, security, adoption, and cost, using system tables, audit logs, billing data, lineage, data-quality monitoring, inference tables, and MLflow tracing.

Must-Have Skills: - Demonstrated experience owning or technically leading an enterprise Databricks platform, extending beyond notebook or pipeline development into architecture, administration, governance, security, lifecycle management, reliability, and enablement. - Broad hands-on Databricks knowledge, with expert depth across several areas such as account and workspace administration, serverless and classic compute, Apache Spark, Delta Lake, Unity Catalog, Lakeflow Jobs and Pipelines, Databricks SQL, Photon, AI/BI, MLflow, and Model Serving.

- Proven cost-management and observability experience, including compute rightsizing, serverless adoption, SQL warehouse optimization, Photon, budgets, tagging, showback or chargeback, system billing data, system tables, operational telemetry, dashboards, alerts, and cost-anomaly investigation. - Strong understanding of Databricks AI/ML foundations, including MLflow, Models in Unity Catalog, feature engineering or Feature Store, batch and real-time inference, Model Serving, inference tables, model monitoring, MLOps, RAG, agent evaluation, AI security, and LLM cost and performance considerations. - Strong Python, PySpark, SQL, automation, and platform API skills. Hands-on experience with Databricks SDKs, REST APIs, CLI, Terraform, Git, CI/CD, automated testing, controlled promotion, rollback, secrets management, and Declarative Automation Bundles or equivalent deployment patterns.

Good-to-Have Skills: - Experience with current Databricks agent capabilities, including Agent Bricks, custom agents, Knowledge Assistant, Supervisor Agent, AI Playground, tool calling, multi-agent systems, and Model Context Protocol integrations. - Experience building governed RAG solutions with Databricks AI Search-formerly Vector Search-and MLflow for GenAI, including embeddings, hybrid retrieval, reranking, tracing, evaluation datasets, LLM judges, human feedback, production monitoring, and quality, latency, and cost analysis.

- Experience with current Databricks agent capabilities, including Agent Bricks, custom agents, Knowledge Assistant, Supervisor Agent, AI Playground, tool calling, multi-agent systems, and Model Context Protocol integrations. - Experience building governed RAG solutions with Databricks AI Search-formerly Vector Search-and MLflow for GenAI, including embeddings, hybrid retrieval, reranking, tracing, evaluation datasets, LLM judges, human feedback, production monitoring, and quality, latency, and cost analysis. - Experience with Unity Gateway or AI Gateway, Foundation Model APIs, external models, AI Functions, batch inference, custom Model Serving endpoints, feature serving, provider routing, rate limits, budgets, service policies, guardrails, auditing, and token-cost attribution.

- Experience with Databricks AI/BI dashboards, Genie Agents, Genie Code, Databricks Apps, natural-language analytics, semantic metadata, AI-generated SQL controls, and responsible-AI practices such as prompt-injection defense, tool authorization, data-leakage prevention, red teaming, and human oversight. - Hands-on experience integrating Databricks with Collibra and working with data-quality or observability products such as Ataccama, Monte Carlo, Datadog, or Splunk. - Experience developing self-service portals, Python microservices, and secure platform APIs, including collaboration with React teams and deployment using Docker, Kubernetes, or Amazon EKS.

- Experience with Lakehouse Federation, Delta Sharing or OpenSharing, Clean Rooms, Databricks Marketplace, Apache Iceberg interoperability, external lineage, SQL/NoSQL/vector databases, Lakebase/Postgres, or Azure and GCP Databricks environments. - Experience supporting data and AI platforms in life sciences or another regulated industry. Preferred certifications include::- Databricks Certified Data Engineer Professional - Databricks Certified Machine Learning Professional

- Experience supporting data and AI platforms in life sciences or another regulated industry. Preferred certifications include::- Databricks Certified Data Engineer Professional - Databricks Certified Machine Learning Professional - Databricks Certified Generative AI Engineer Associate

- Databricks Certified Machine Learning Professional - Databricks Certified Generative AI Engineer Associate - AWS Certified Data Engineer - Associate

About Amgen

Amgen harnesses the best of biology and technology to fight the world’s toughest diseases, and make people’s lives easier, fuller and longer.

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Job description

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Career Category

Engineering

Job Description

Join Amgen’s Mission of Serving Patients

At Amgen, if you feel like you’re part of something bigger, it’s because you are. Our shared mission-to serve patients living with serious illnesses-drives all that we do.

Since 1980, we’ve helped pioneer the world of biotech in our fight against the world’s toughest diseases. With our focus on four therapeutic areas -Oncology, Inflammation, General Medicine, and Rare Disease- we reach millions of patients each year. As a member of the Amgen team, you’ll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives.

Our award-winning culture is collaborative, innovative, and science based. If you have a passion for challenges and the opportunities that lay within them, you’ll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career.

Senior Databricks Platform Engineer

About Amgen

Amgen harnesses the best of biology and technology to fight the world’s toughest diseases, and make people’s lives easier, fuller and longer. We discover, develop, manufacture and deliver innovative medicines to help millions of patients. Amgen helped establish the biotechnology industry more than 40 years ago and remains on the cutting-edge of innovation, using technology and human genetic data to push beyond what’s known today.

About the Role

This is a senior individual-contributor and technical-leadership role. The successful candidate will combine deep Databricks platform expertise with strong AWS, security, governance, FinOps, observability, automation, and AI/ML knowledge. The role will translate business and technology needs into secure, scalable, reusable, observable, and cost-efficient platform capabilities.

This role enables delivery teams through paved-road patterns, frameworks, APIs, automation, reference implementations, architecture reviews, and expert guidance. It does not own the architecture or implementation of business-specific data pipelines.

What you will do

Roles & Responsibilities:

Serve as the Databricks platform lead, shaping the platform roadmap, reference architecture, standards, operating model, guardrails, lifecycle strategy, and capability backlog in partnership with Enterprise Data Architecture, security, governance, operations, and delivery teams.
Evaluate Databricks features and third-party technologies through structured assessments and PoCs. Assess architectural fit, security, privacy, compliance, interoperability, performance, reliability, cost, supportability, and user experience before recommending adoption, restriction, deferral, or rejection.
Lead architecture reviews covering account, workspace, metastore, catalog, networking, storage, compute, SQL, ML/AI, dev/test/prod isolation, regional deployment, serverless and classic compute, capacity, upgrades, high availability, disaster recovery, and RTO/RPO requirements.
Build reusable frameworks, accelerators, platform APIs, custom templates, provisioning workflows, policy-as-code controls, and reference implementations using Terraform, Databricks SDKs and REST APIs, the Databricks CLI, and Declarative Automation Bundles-formerly Databricks Asset Bundles.
Design and operationalize integrations between Databricks and third party services like Collibra, enabling enterprise metadata, classification, ownership, lineage, business glossary, data-quality, AI-asset, and governed-tag capabilities.
Define and enforce security guardrails covering identity federation, SSO/SCIM, OAuth, service principals, least privilege, segregation of duties, Unity Catalog access controls and ABAC, secrets, encryption, private connectivity, egress controls, auditing, and data-exfiltration prevention.
Lead Databricks FinOps, including tagging, allocation, showback or chargeback, budgets, alerts, compute policies, serverless usage policies, cost-anomaly detection, and model or token cost controls. Optimize spend through rightsizing, Photon, autoscaling, auto-termination, workload isolation, and appropriate compute selection.
Establish platform observability through SLOs, KPIs, telemetry, dashboards, and alerts for availability, reliability, jobs, queries, compute, SQL warehouses, capacity, security, adoption, and cost, using system tables, audit logs, billing data, lineage, data-quality monitoring, inference tables, and MLflow tracing.
Enable governed ML, GenAI, RAG, and agent capabilities, including feature and model lifecycle, Model Serving, AI Search, foundation-model access, evaluation, monitoring, and LLMOps. Define responsible-AI guardrails for models, agents, prompts, retrieval components, MCP tools, and external providers.
Improve platform reliability through root-cause analysis, operational reviews, automated remediation, runbooks, resilience testing, and elimination of recurring incidents.
Drive user enablement through onboarding, documentation, reference solutions, workshops, office hours, and best-practice communities. Translate strategic-program requirements into reusable platform capabilities and measure improvements in adoption, developer experience, governance, security, reliability, and cost.

What we expect of you

Basic Qualifications and Experience:

Master’s or Bachelor’s degree in computer science or engineering field and 9 to12 years of relevant experience

Must-Have Skills:

Demonstrated experience owning or technically leading an enterprise Databricks platform, extending beyond notebook or pipeline development into architecture, administration, governance, security, lifecycle management, reliability, and enablement.
Broad hands-on Databricks knowledge, with expert depth across several areas such as account and workspace administration, serverless and classic compute, Apache Spark, Delta Lake, Unity Catalog, Lakeflow Jobs and Pipelines, Databricks SQL, Photon, AI/BI, MLflow, and Model Serving.
Proven ability to design enterprise account, workspace, metastore, catalog, dev/test/prod, workload-isolation, promotion, high-availability, disaster-recovery, and capacity strategies, and to establish enforceable platform standards and lifecycle controls.
Strong Unity Catalog and security expertise, including privileges, ownership, managed and external storage, governed tags, lineage, workspace bindings, row filters, column masks, ABAC, SSO/SCIM, OAuth, service principals, secrets, encryption, private connectivity, auditing, and data-exfiltration controls.
Strong AWS experience with IAM, VPC, PrivateLink, S3, EC2, KMS, CloudWatch, CloudTrail, Secrets Manager, and STS. Familiarity with EKS, Lambda, Glue, EMR, and RDS is beneficial.
Proven cost-management and observability experience, including compute rightsizing, serverless adoption, SQL warehouse optimization, Photon, budgets, tagging, showback or chargeback, system billing data, system tables, operational telemetry, dashboards, alerts, and cost-anomaly investigation.
Strong understanding of Databricks AI/ML foundations, including MLflow, Models in Unity Catalog, feature engineering or Feature Store, batch and real-time inference, Model Serving, inference tables, model monitoring, MLOps, RAG, agent evaluation, AI security, and LLM cost and performance considerations.
Strong Python, PySpark, SQL, automation, and platform API skills. Hands-on experience with Databricks SDKs, REST APIs, CLI, Terraform, Git, CI/CD, automated testing, controlled promotion, rollback, secrets management, and Declarative Automation Bundles or equivalent deployment patterns.
Strong Spark and SQL troubleshooting and performance-analysis skills, including query plans, partitioning, shuffles, skew, file sizing, table optimization, concurrency, cluster utilization, and Photon-enabled workloads.
Strong software-engineering and distributed-systems fundamentals, including modular and API design, version control, testing, secure coding, maintainability, production support, technical documentation, relational and dimensional modeling, operational readiness, and recovery patterns.
Strong analytical problem-solving skills and experience working in Agile environments using tools such as Jira or Jira Align.

Good-to-Have Skills:

Experience with current Databricks agent capabilities, including Agent Bricks, custom agents, Knowledge Assistant, Supervisor Agent, AI Playground, tool calling, multi-agent systems, and Model Context Protocol integrations.
Experience building governed RAG solutions with Databricks AI Search-formerly Vector Search-and MLflow for GenAI, including embeddings, hybrid retrieval, reranking, tracing, evaluation datasets, LLM judges, human feedback, production monitoring, and quality, latency, and cost analysis.
Experience with Unity Gateway or AI Gateway, Foundation Model APIs, external models, AI Functions, batch inference, custom Model Serving endpoints, feature serving, provider routing, rate limits, budgets, service policies, guardrails, auditing, and token-cost attribution.
Experience with Databricks AI/BI dashboards, Genie Agents, Genie Code, Databricks Apps, natural-language analytics, semantic metadata, AI-generated SQL controls, and responsible-AI practices such as prompt-injection defense, tool authorization, data-leakage prevention, red teaming, and human oversight.
Hands-on experience integrating Databricks with Collibra and working with data-quality or observability products such as Ataccama, Monte Carlo, Datadog, or Splunk.
Experience developing self-service portals, Python microservices, and secure platform APIs, including collaboration with React teams and deployment using Docker, Kubernetes, or Amazon EKS.
Experience with Lakehouse Federation, Delta Sharing or OpenSharing, Clean Rooms, Databricks Marketplace, Apache Iceberg interoperability, external lineage, SQL/NoSQL/vector databases, Lakebase/Postgres, or Azure and GCP Databricks environments.
Experience supporting data and AI platforms in life sciences or another regulated industry. Preferred certifications include::
- Databricks Certified Data Engineer Professional
- Databricks Certified Machine Learning Professional
- Databricks Certified Generative AI Engineer Associate
- AWS Certified Data Engineer - Associate
- AWS Certified Solutions Architect
- AWS Certified Security - Specialty
- AWS Certified Machine Learning Engineer - Associate
- SAFe Agilist or another SAFe certification

Functional Skills:

Excellent written and verbal communication, with the ability to explain complex platform concepts, architecture decisions, risks, and trade-offs in clear, business-relevant language.
Strong influencing and consensus-building skills, including the ability to establish standards and drive adoption across teams without relying solely on formal authority.
A platform-product mindset focused on reusable capabilities, paved roads, developer experience, measurable outcomes, and long-term platform health.
Strong systems-thinking and structured problem-solving skills, with the ability to diagnose issues across application, platform, cloud, governance, security, and operating-model boundaries.
High degree of ownership, initiative, and follow-through, with the ability to move ambiguous topics from exploration through decision, implementation, adoption, and continuous improvement.
Collaborative and globally minded, with experience working effectively across architecture, governance, cybersecurity, operations, engineering, product, and business teams.
Strong planning, estimation, prioritization, and execution skills, with the ability to manage multiple initiatives while maintaining high standards for security, reliability, quality, and reusability.
Ability to balance innovation with enterprise risk, distinguishing between experimentation, limited preview adoption, and production-ready capabilities.
Strong coaching and enablement skills, with the ability to create clear documentation, facilitate technical workshops, mentor engineers, and build an active platform community.
A growth mindset and commitment to continuous learning, modern engineering practices, constructive challenge, and responsible adoption of AI.

What you can expect of us

As we work to develop treatments that take care of others, we also work to care for your professional and personal growth and well-being. From our competitive benefits to our collaborative culture, we’ll support your journey every step of the way.

In addition to the base salary, Amgen offers competitive and comprehensive Total Rewards Plans that are aligned with local industry standards.

Apply now

EQUAL OPPORTUNITY STATEMENT

Amgen is an Equal Opportunity employer and will consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or any other basis protected by applicable law.

We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

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Please note that you should be in your current position for at least 18 months before applying to internal positions. Staff must notify their current manager if invited for an interview. In addition, Staff are ineligible to apply for open positions if (a) their performance is currently being managed on a performance improvement plan (PIP) or other locally utilized formal coaching document or (b) their most recent performance rating was not a “Partially Meets Expectations” or higher. Please visit our Internal Transfer Guidelines for more detailed information

GCF Level

GCF Level 05

Career Category

Engineering

Position Type

Full time

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