Senior AI engineer-Data Databricks Platform
Reads like a Data Engineer role: pipelines and data infrastructure.
Bangalore, Karnātaka, India
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingWe are seeking a highly skilled Databricks Platform Engineer to design, build, configure, integrate, and operationalize enterprise Data & AI solutions on the Databricks platform.
The ideal candidate will possess deep expertise in Databricks, cloud platforms, data engineering, MLOps, platform automation, and enterprise integration patterns.
Design and maintain Databricks workspaces across dev, test, and production
Build scalable batch and streaming data pipelines on the Lakehouse
Automate platform deployment with Terraform and CI/CD pipelines
Enable ML and generative AI workloads with MLflow and model serving
Implement security controls, Unity Catalog governance, and audit logging
From the employer’s posting
We are seeking a highly skilled Databricks Platform Engineer to design, build, configure, integrate, and operationalize enterprise Data & AI solutions on the Databricks platform. This role will be responsible for establishing scalable, secure, and reliable data and AI capabilities, enabling data engineers, data scientists, AI engineers, and business teams to accelerate innovation and deliver business value.
The ideal candidate will possess deep expertise in Databricks, cloud platforms, data engineering, MLOps, platform automation, and enterprise integration patterns.
Design, build, configure, and maintain Databricks workspaces across development, testing, and production environments.
Build and integrate scalable data pipelines using Databricks Lakehouse architecture.
Automate platform deployment and configuration using Infrastructure as Code (Terraform, CI/CD pipelines).
Enable ML and Generative AI workloads on Databricks. Implement MLflow, model lifecycle management, feature stores, and model serving capabilities.
Implement enterprise security controls, RBAC, data masking, encryption, and audit logging.
What you’ll bring
All qualificationsCore experience
- Bachelor's or master’s degree in computer science, Engineering, IT, or a related field.
- 5+ years of experience in Data, Platform, or Cloud Engineering.
- 3+ years of hands-on experience with the Databricks Lakehouse Platform.
- Strong expertise in Delta Lake, Unity Catalog, MLflow, Databricks Workflows, Structured Streaming, PySpark, and Spark SQL.
- Experience working with cloud platforms such as Azure, AWS, or GCP.
- Proficiency in Python and SQL.
Preferred experience
- Experience with Generative AI, Agentic AI, and LLM-based applications.
- Experience integrating Databricks with Snowflake and enterprise AI platforms.
- Knowledge of Kubernetes, Docker, APIs, Kafka, and event-driven architectures.
- Experience supporting enterprise-scale Data & AI platforms.
Qualification wording
Bachelor's or master’s degree in computer science, Engineering, IT, or a related field.
5+ years of experience in Data, Platform, or Cloud Engineering.
3+ years of hands-on experience with the Databricks Lakehouse Platform.
Strong expertise in Delta Lake, Unity Catalog, MLflow, Databricks Workflows, Structured Streaming, PySpark, and Spark SQL.
Experience working with cloud platforms such as Azure, AWS, or GCP.
Proficiency in Python and SQL.
Experience with Generative AI, Agentic AI, and LLM-based applications.
Experience integrating Databricks with Snowflake and enterprise AI platforms.
Knowledge of Kubernetes, Docker, APIs, Kafka, and event-driven architectures.
Experience supporting enterprise-scale Data & AI platforms.
Education & alternatives
Required Qualifications - Bachelor's or master’s degree in computer science, Engineering, IT, or a related field. - 5+ years of experience in Data, Platform, or Cloud Engineering.
Tools in this posting
- Python
- SQL
- AWS
- Azure
- Databricks
- Delta Lake
- Docker
- GCP
- Kafka
- Kubernetes
- MLflow
- Oracle
- Snowflake
- Spark
- SQL Server
- Terraform
- PySpark
Source — Tool mentions in context
- Experience working with cloud platforms such as Azure, AWS, or GCP. - Proficiency in Python and SQL. - Strong knowledge of DevOps, CI/CD, Infrastructure as Code (IaC), and platform automation.
- Implement platform standards, reusable frameworks, templates, and best practices. - Manage Unity Catalog, clusters, SQL Warehouses, compute policies, workspace configurations, and access controls. - Automate platform deployment and configuration using Infrastructure as Code (Terraform, CI/CD pipelines).
- Implement Delta Lake, Structured Streaming, and medallion architecture patterns. - Integrate Databricks with enterprise data platforms such as Snowflake, SAP, Oracle, SQL Server, Azure Data Factory, Kafka, and cloud storage services. AI & Machine Learning Enablement
- 3+ years of hands-on experience with the Databricks Lakehouse Platform. - Strong expertise in Delta Lake, Unity Catalog, MLflow, Databricks Workflows, Structured Streaming, PySpark, and Spark SQL. - Experience working with cloud platforms such as Azure, AWS, or GCP.
- Strong expertise in Delta Lake, Unity Catalog, MLflow, Databricks Workflows, Structured Streaming, PySpark, and Spark SQL. - Experience working with cloud platforms such as Azure, AWS, or GCP. - Proficiency in Python and SQL.
- Strong knowledge of DevOps, CI/CD, Infrastructure as Code (IaC), and platform automation. - Hands-on experience with Terraform, GitHub Actions, Azure DevOps, or equivalent CI/CD tools. Preferred Qualifications
Job Description: Databricks Platform Engineer Position Summary
Position Summary We are seeking a highly skilled Databricks Platform Engineer to design, build, configure, integrate, and operationalize enterprise Data & AI solutions on the Databricks platform. This role will be responsible for establishing scalable, secure, and reliable data and AI capabilities, enabling data engineers, data scientists, AI engineers, and business teams to accelerate innovation and deliver business value. The ideal candidate will possess deep expertise in Databricks, cloud platforms, data engineering, MLOps, platform automation, and enterprise integration patterns.
We are seeking a highly skilled Databricks Platform Engineer to design, build, configure, integrate, and operationalize enterprise Data & AI solutions on the Databricks platform. This role will be responsible for establishing scalable, secure, and reliable data and AI capabilities, enabling data engineers, data scientists, AI engineers, and business teams to accelerate innovation and deliver business value. The ideal candidate will possess deep expertise in Databricks, cloud platforms, data engineering, MLOps, platform automation, and enterprise integration patterns. Key Responsibilities
Platform Engineering & Administration - Design, build, configure, and maintain Databricks workspaces across development, testing, and production environments. - Implement platform standards, reusable frameworks, templates, and best practices.
Data Engineering & Integration - Build and integrate scalable data pipelines using Databricks Lakehouse architecture. - Design ingestion frameworks for batch, streaming, API, database, and file-based integrations.
AI & Machine Learning Enablement - Enable ML and Generative AI workloads on Databricks. - Implement MLflow, model lifecycle management, feature stores, and model serving capabilities.
- Support AI engineers and data scientists with scalable development environments. - Integrate Databricks with LLMs, vector databases, AI gateways, and enterprise AI platforms. DevOps, DataOps & MLOps
- 5+ years of experience in Data, Platform, or Cloud Engineering. - 3+ years of hands-on experience with the Databricks Lakehouse Platform. - Strong expertise in Delta Lake, Unity Catalog, MLflow, Databricks Workflows, Structured Streaming, PySpark, and Spark SQL.
- Experience with Generative AI, Agentic AI, and LLM-based applications. - Experience integrating Databricks with Snowflake and enterprise AI platforms. - Knowledge of Kubernetes, Docker, APIs, Kafka, and event-driven architectures.
- Knowledge of Kubernetes, Docker, APIs, Kafka, and event-driven architectures. - Databricks Certified Professional or Associate certifications. - Experience supporting enterprise-scale Data & AI platforms.
Ideal Candidate Profile A hands-on engineer who can build, configure, integrate, automate, secure, and operationalize Databricks as an enterprise Data & AI platform while enabling scalable DataOps, MLOps, and AI solutions across the organization. One-line executive summary: Own and engineer the Databricks platform end-to-end, enabling enterprise-scale Data, AI, ML, and Agentic AI solutions through automation, integration, governance, security, and operational excellence.
A hands-on engineer who can build, configure, integrate, automate, secure, and operationalize Databricks as an enterprise Data & AI platform while enabling scalable DataOps, MLOps, and AI solutions across the organization. One-line executive summary: Own and engineer the Databricks platform end-to-end, enabling enterprise-scale Data, AI, ML, and Agentic AI solutions through automation, integration, governance, security, and operational excellence. Our Commitment to a Culture of Inclusion & Belonging Ecolab is committed to fair and equal treatment of associates and applicants and furthering the principles of Equal Opportunity to Employment. We will recruit, hire, promote, transfer and provide opportunities for advancement based on individual qualifications and job performance in all matters affecting employment, compensation, benefits, working conditions, and opportunities for advancement. Ecolab will not discriminate against any associate or applicant for employment because of race, religion, color, creed, national origin,citizenship status, sex, sexual orientation, gender identity and expressions, genetic information, marital status, age, or disability.
- Design ingestion frameworks for batch, streaming, API, database, and file-based integrations. - Implement Delta Lake, Structured Streaming, and medallion architecture patterns. - Integrate Databricks with enterprise data platforms such as Snowflake, SAP, Oracle, SQL Server, Azure Data Factory, Kafka, and cloud storage services.
- Experience integrating Databricks with Snowflake and enterprise AI platforms. - Knowledge of Kubernetes, Docker, APIs, Kafka, and event-driven architectures. - Databricks Certified Professional or Associate certifications.
- Enable ML and Generative AI workloads on Databricks. - Implement MLflow, model lifecycle management, feature stores, and model serving capabilities. - Support AI engineers and data scientists with scalable development environments.
- Manage Unity Catalog, clusters, SQL Warehouses, compute policies, workspace configurations, and access controls. - Automate platform deployment and configuration using Infrastructure as Code (Terraform, CI/CD pipelines). Data Engineering & Integration
Posting record
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- Live
- First seen by us
- Sep 21, 2026 · 39 days ago
- Recorded sightings
- 133
- Last seen by us
- Oct 31, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
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Job description
Job Description: Databricks Platform Engineer
Position Summary
We are seeking a highly skilled Databricks Platform Engineer to design, build, configure, integrate, and operationalize enterprise Data & AI solutions on the Databricks platform. This role will be responsible for establishing scalable, secure, and reliable data and AI capabilities, enabling data engineers, data scientists, AI engineers, and business teams to accelerate innovation and deliver business value.
The ideal candidate will possess deep expertise in Databricks, cloud platforms, data engineering, MLOps, platform automation, and enterprise integration patterns.
Key Responsibilities
Platform Engineering & Administration
- Design, build, configure, and maintain Databricks workspaces across development, testing, and production environments.
- Implement platform standards, reusable frameworks, templates, and best practices.
- Manage Unity Catalog, clusters, SQL Warehouses, compute policies, workspace configurations, and access controls.
- Automate platform deployment and configuration using Infrastructure as Code (Terraform, CI/CD pipelines).
Data Engineering & Integration
- Build and integrate scalable data pipelines using Databricks Lakehouse architecture.
- Design ingestion frameworks for batch, streaming, API, database, and file-based integrations.
- Implement Delta Lake, Structured Streaming, and medallion architecture patterns.
- Integrate Databricks with enterprise data platforms such as Snowflake, SAP, Oracle, SQL Server, Azure Data Factory, Kafka, and cloud storage services.
AI & Machine Learning Enablement
- Enable ML and Generative AI workloads on Databricks.
- Implement MLflow, model lifecycle management, feature stores, and model serving capabilities.
- Support AI engineers and data scientists with scalable development environments.
- Integrate Databricks with LLMs, vector databases, AI gateways, and enterprise AI platforms.
DevOps, DataOps & MLOps
- Establish CI/CD pipelines for data and AI workloads.
- Implement automated testing, deployment, monitoring, and rollback mechanisms.
- Create reusable deployment frameworks and engineering accelerators.
- Support release management and environment promotion processes.
Security, Governance & Compliance
- Implement enterprise security controls, RBAC, data masking, encryption, and audit logging.
- Configure and manage Unity Catalog governance policies.
- Ensure compliance with enterprise security, privacy, and regulatory requirements.
- Partner with cybersecurity teams to implement platform hardening and vulnerability remediation.
Monitoring & Reliability Engineering
- Implement platform observability, monitoring, and operational dashboards.
- Configure logging, alerting, performance monitoring, and incident management processes.
- Optimize platform performance, cost, scalability, and reliability.
- Support production operations and resolve platform issues.
Collaboration & Technical Leadership
- Collaborate with architects, data engineers, AI engineers, security teams, and business stakeholders.
- Provide technical guidance and platform best practices.
- Participate in architecture reviews and platform roadmap planning.
- Mentor junior engineers and contribute to engineering excellence initiatives.
Required Qualifications
- Bachelor's or master’s degree in computer science, Engineering, IT, or a related field.
- 5+ years of experience in Data, Platform, or Cloud Engineering.
- 3+ years of hands-on experience with the Databricks Lakehouse Platform.
- Strong expertise in Delta Lake, Unity Catalog, MLflow, Databricks Workflows, Structured Streaming, PySpark, and Spark SQL.
- Experience working with cloud platforms such as Azure, AWS, or GCP.
- Proficiency in Python and SQL.
- Strong knowledge of DevOps, CI/CD, Infrastructure as Code (IaC), and platform automation.
- Hands-on experience with Terraform, GitHub Actions, Azure DevOps, or equivalent CI/CD tools.
Preferred Qualifications
- Experience with Generative AI, Agentic AI, and LLM-based applications.
- Experience integrating Databricks with Snowflake and enterprise AI platforms.
- Knowledge of Kubernetes, Docker, APIs, Kafka, and event-driven architectures.
- Databricks Certified Professional or Associate certifications.
- Experience supporting enterprise-scale Data & AI platforms.
Key Success Metrics
- Platform availability and reliability.
- Deployment automation and operational efficiency.
- Security and compliance adherence.
- Data pipeline performance and scalability.
- AI/ML platform adoption and productivity improvements.
- Platform cost optimization and governance effectiveness.
Ideal Candidate Profile
A hands-on engineer who can build, configure, integrate, automate, secure, and operationalize Databricks as an enterprise Data & AI platform while enabling scalable DataOps, MLOps, and AI solutions across the organization.
One-line executive summary: Own and engineer the Databricks platform end-to-end, enabling enterprise-scale Data, AI, ML, and Agentic AI solutions through automation, integration, governance, security, and operational excellence.
Our Commitment to a Culture of Inclusion & Belonging
Ecolab is committed to fair and equal treatment of associates and applicants and furthering the principles of Equal Opportunity to Employment. We will recruit, hire, promote, transfer and provide opportunities for advancement based on individual qualifications and job performance in all matters affecting employment, compensation, benefits, working conditions, and opportunities for advancement. Ecolab will not discriminate against any associate or applicant for employment because of race, religion, color, creed, national origin,citizenship status, sex, sexual orientation, gender identity and expressions, genetic information, marital status, age, or disability.
Source details
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- Location & working pattern
Bangalore, Karnātaka, India
Working pattern and location restrictions need checking in the full posting.
- Work authorization
One-line executive summary: Own and engineer the Databricks platform end-to-end, enabling enterprise-scale Data, AI, ML, and Agentic AI solutions through automation, integration, governance, security, and operational excellence. Our Commitment to a Culture of Inclusion & Belonging Ecolab is committed to fair and equal treatment of associates and applicants and furthering the principles of Equal Opportunity to Employment. We will recruit, hire, promote, transfer and provide opportunities for advancement based on individual qualifications and job performance in all matters affecting employment, compensation, benefits, working conditions, and opportunities for advancement. Ecolab will not discriminate against any associate or applicant for employment because of race, religion, color, creed, national origin,citizenship status, sex, sexual orientation, gender identity and expressions, genetic information, marital status, age, or disability.
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