Principal Data Architect - Multi-Cloud (GCP & Azure)

Hyderabad

Pay: Pay not listed
Work setting: Hybrid — work setting source
Work setup in the employer’s posting: hybrid
From the employer’s posting
Employment: Full-time — employment source
Employment type Full Time
Employment type in the employer’s posting: Full Time
From the employer’s posting

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What you’ll work on

Full posting

Design scalable Data Lakehouse solutions across GCP and Azure
Build agent-driven pipelines to migrate on-prem legacy warehouses to the cloud
Manage the customer technical backlog and delivery roadmap
Set governance standards for cataloging, lineage and data quality
Mentor senior and mid-level engineers and run architecture reviews

From the employer’s posting

Lead the end-to-end architecture, design, and implementation of scalable Data Lakehouse solutions across Google Cloud Platform

Architect and deploy automated, scalable, agent-driven data pipelines

Own and actively manage the customer technical backlog

Establish and enforce best practices for unified data cataloging, metadata management, automated lineage, and data quality

mentor senior and mid-level engineers, conduct architecture review

What you’ll bring

All qualifications

Core experience

Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, or equivalent practical experience.

Qualification wording

Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, or equivalent practical experience.

Education & alternatives

Education - Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, or equivalent practical experience. Employment type

Tools in this posting

C
Python
SQL
AWS
Azure
BigQuery
Databricks
Delta Lake
GCP
Google Cloud Storage
Kafka
Kubernetes
MongoDB
NoSQL
Oracle
PostgreSQL
Redis
Redshift
S3
Snowflake
Spark
SQL Server
Terraform
Airflow
FastAPI
Flask
PySpark

Source

- Strong leadership capability in technical backlog management, agile scoping, sprint planning, and setting technical strategy for distributed teams. - Exceptional stakeholder communication skills: capability to articulate architectural trade-offs to C-level executives as smoothly as reviewing code with engineers. Preferred Qualifications (Optional / Good to Have)

- Containerization & Deployment: Kubernetes (GKE and AKS), Cloud Run, Azure Container Apps. - API Frameworks: Python (FastAPI, Flask) for data service endpoints. - Databases & Querying: Advanced SQL validation and extraction from legacy/RDBMS systems (SQL Server, Oracle, PostgreSQL).

- Multi-Cloud Lakehouse Architecture: Lead the end-to-end architecture, design, and implementation of scalable Data Lakehouse solutions across Google Cloud Platform (BigQuery, GCS, BigLake, Dataplex) and Microsoft Azure (Azure Synapse Analytics, Microsoft Fabric, ADLS Gen2, Azure Databricks). - On-Prem to Cloud Migration & Agentic Pipelines: Architect and deploy automated, scalable, agent-driven data pipelines (leveraging GenAI agents and metadata-driven frameworks) to orchestrate, validate, and accelerate complex migrations from on-premises legacy warehouses/databases (Oracle, Teradata, SQL Server) to GCP and Azure. - Client Engagement & Advisory: Collaborate directly with customers to understand business goals, data challenges, and technical requirements; translate them into robust architectural blueprints, technical roadmaps, and actionable delivery plans.

- API Frameworks: Python (FastAPI, Flask) for data service endpoints. - Databases & Querying: Advanced SQL validation and extraction from legacy/RDBMS systems (SQL Server, Oracle, PostgreSQL). - CI/CD & Version Control: Git, GitHub, GitLab CI/CD, Azure DevOps, and Infrastructure as Code (Terraform).

- Hands-on knowledge of Snowflake and Databricks - Familiarity with AWS data services (Amazon S3, Redshift, Glue, EMR). - Experience with LangChain, LlamaIndex, or agentic framework integrations in data operations.

We are seeking a Principal Data Architect to define and drive enterprise data architecture, modernization, and governance strategies across complex multi-cloud (Google Cloud Platform and Microsoft Azure) and hybrid environments. This role will design scalable, secure, and high-performing data platforms while partnering with business and engineering leaders to translate strategic objectives into impactful data solutions. A key focus of this role will be spearheading scalable, agent-driven data migration pipelines to transition legacy on-premises data assets to the cloud, setting the technical direction for engineering teams, and actively managing the customer's technical backlog and delivery roadmap.

Key Responsibilities - Multi-Cloud Lakehouse Architecture: Lead the end-to-end architecture, design, and implementation of scalable Data Lakehouse solutions across Google Cloud Platform (BigQuery, GCS, BigLake, Dataplex) and Microsoft Azure (Azure Synapse Analytics, Microsoft Fabric, ADLS Gen2, Azure Databricks). - On-Prem to Cloud Migration & Agentic Pipelines: Architect and deploy automated, scalable, agent-driven data pipelines (leveraging GenAI agents and metadata-driven frameworks) to orchestrate, validate, and accelerate complex migrations from on-premises legacy warehouses/databases (Oracle, Teradata, SQL Server) to GCP and Azure.

- Customer Technical Backlog Management: Own and actively manage the customer technical backlog—translating business epics into technical user stories, prioritizing technical debt, defining sprint milestones, and aligning backlog items with business ROI. - Modern Ingestion & Streaming: Design and implement data pipelines supporting both real-time and batch ingestion using modern orchestration (Cloud Composer, Azure Data Factory, Airflow) and streaming/messaging frameworks (Kafka, GCP Pub/Sub, Azure Event Hubs). - Governance & Lineage: Establish and enforce best practices for unified data cataloging, metadata management, automated lineage, and data quality across multi-cloud and on-prem systems using Dataplex, Microsoft Purview, or open-source governance tools.

- Governance & Lineage: Establish and enforce best practices for unified data cataloging, metadata management, automated lineage, and data quality across multi-cloud and on-prem systems using Dataplex, Microsoft Purview, or open-source governance tools. - Security & Compliance: Define and implement robust data security, identity management (IAM / Azure Entra ID), encryption, row/column-level access control, and regulatory compliance standards across clouds. - Team Technical Leadership & Mentorship: Steer the overarching technical direction of engineering teams; establish architectural standards, mentor senior and mid-level engineers, conduct architecture review, and ensure engineering excellence.

Candidates should demonstrate hands-on architecture and design experience across the following domains: - Cloud Platforms: Deep expertise in both Google Cloud Platform (GCP) and Microsoft Azure enterprise data ecosystems. - Processing Engines: Apache Spark, PySpark, Databricks, Azure Synapse / Fabric Spark, GCP Dataproc.

- Cloud Platforms: Deep expertise in both Google Cloud Platform (GCP) and Microsoft Azure enterprise data ecosystems. - Processing Engines: Apache Spark, PySpark, Databricks, Azure Synapse / Fabric Spark, GCP Dataproc. - Modern Migration & AI Automation: Building automated, agentic, or metadata-driven data migration pipelines, automated schema conversion, and data validation agents for on-prem to cloud lift-and-shift/re-platforming.

- Modern Migration & AI Automation: Building automated, agentic, or metadata-driven data migration pipelines, automated schema conversion, and data validation agents for on-prem to cloud lift-and-shift/re-platforming. - Messaging & Streaming: Apache Kafka, GCP Pub/Sub, Azure Event Hubs. - Orchestration: Apache Airflow, Google Cloud Composer, Azure Data Factory (ADF).

- Messaging & Streaming: Apache Kafka, GCP Pub/Sub, Azure Event Hubs. - Orchestration: Apache Airflow, Google Cloud Composer, Azure Data Factory (ADF). - Analytical Storage & Warehouses: GCP BigQuery, Azure Synapse, Microsoft Fabric, Delta Lake/Parquet.

- Orchestration: Apache Airflow, Google Cloud Composer, Azure Data Factory (ADF). - Analytical Storage & Warehouses: GCP BigQuery, Azure Synapse, Microsoft Fabric, Delta Lake/Parquet. - NoSQL / Cache Storage: MongoDB, Redis, Bigtable, Azure Cosmos DB.

- Analytical Storage & Warehouses: GCP BigQuery, Azure Synapse, Microsoft Fabric, Delta Lake/Parquet. - NoSQL / Cache Storage: MongoDB, Redis, Bigtable, Azure Cosmos DB. - Containerization & Deployment: Kubernetes (GKE and AKS), Cloud Run, Azure Container Apps.

- NoSQL / Cache Storage: MongoDB, Redis, Bigtable, Azure Cosmos DB. - Containerization & Deployment: Kubernetes (GKE and AKS), Cloud Run, Azure Container Apps. - API Frameworks: Python (FastAPI, Flask) for data service endpoints.

- Databases & Querying: Advanced SQL validation and extraction from legacy/RDBMS systems (SQL Server, Oracle, PostgreSQL). - CI/CD & Version Control: Git, GitHub, GitLab CI/CD, Azure DevOps, and Infrastructure as Code (Terraform). Required Qualifications & Skills

- 15+ years of progressive experience in Software Engineering, Data Engineering, and Enterprise Data Platform architecture. - 5+ years architecting, migrating, and deploying production data platforms across GCP and/or Azure, with proven multi-cloud integration patterns. - Demonstrated track record leading on-premises/cloud to cloud modernization/migration initiatives, including data ingestion, transformation, automated data reconciliation, and cutover strategies.

Preferred Qualifications (Optional / Good to Have) - Multi-cloud certifications: Google Cloud Professional Data Engineer / Cloud Architect and/or Microsoft Certified: Azure Data Engineer Associate / Azure Solutions Architect Expert. - Hands-on knowledge of Snowflake and Databricks

- Multi-cloud certifications: Google Cloud Professional Data Engineer / Cloud Architect and/or Microsoft Certified: Azure Data Engineer Associate / Azure Solutions Architect Expert. - Hands-on knowledge of Snowflake and Databricks - Familiarity with AWS data services (Amazon S3, Redshift, Glue, EMR).

Posting record

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Actively hiringFirst seen by us 11 days ago, still listed, and not a repost of an earlier posting.

Status in our records: Live
First seen by us: Oct 19, 2026 · 11 days ago
Recorded sightings: 11
Last seen by us: Oct 29, 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

View original posting ↗

We are seeking a Principal Data Architect to define and drive enterprise data architecture, modernization, and governance strategies across complex multi-cloud (Google Cloud Platform and Microsoft Azure) and hybrid environments. This role will design scalable, secure, and high-performing data platforms while partnering with business and engineering leaders to translate strategic objectives into impactful data solutions.

A key focus of this role will be spearheading scalable, agent-driven data migration pipelines to transition legacy on-premises data assets to the cloud, setting the technical direction for engineering teams, and actively managing the customer's technical backlog and delivery roadmap.

Key Responsibilities

Multi-Cloud Lakehouse Architecture: Lead the end-to-end architecture, design, and implementation of scalable Data Lakehouse solutions across Google Cloud Platform (BigQuery, GCS, BigLake, Dataplex) and Microsoft Azure (Azure Synapse Analytics, Microsoft Fabric, ADLS Gen2, Azure Databricks).
On-Prem to Cloud Migration & Agentic Pipelines: Architect and deploy automated, scalable, agent-driven data pipelines (leveraging GenAI agents and metadata-driven frameworks) to orchestrate, validate, and accelerate complex migrations from on-premises legacy warehouses/databases (Oracle, Teradata, SQL Server) to GCP and Azure.
Client Engagement & Advisory: Collaborate directly with customers to understand business goals, data challenges, and technical requirements; translate them into robust architectural blueprints, technical roadmaps, and actionable delivery plans.
Customer Technical Backlog Management: Own and actively manage the customer technical backlog—translating business epics into technical user stories, prioritizing technical debt, defining sprint milestones, and aligning backlog items with business ROI.
Modern Ingestion & Streaming: Design and implement data pipelines supporting both real-time and batch ingestion using modern orchestration (Cloud Composer, Azure Data Factory, Airflow) and streaming/messaging frameworks (Kafka, GCP Pub/Sub, Azure Event Hubs).
Governance & Lineage: Establish and enforce best practices for unified data cataloging, metadata management, automated lineage, and data quality across multi-cloud and on-prem systems using Dataplex, Microsoft Purview, or open-source governance tools.
Security & Compliance: Define and implement robust data security, identity management (IAM / Azure Entra ID), encryption, row/column-level access control, and regulatory compliance standards across clouds.
Team Technical Leadership & Mentorship: Steer the overarching technical direction of engineering teams; establish architectural standards, mentor senior and mid-level engineers, conduct architecture review, and ensure engineering excellence.
Hands-on Delivery & Prototyping: Balance strategic architecture discussions with hands-on solutioning, architectural spikes, Proof-of-Concepts (POCs), and performance tuning of complex multi-cloud distributed workloads.
Cross-Functional Partnership: Partner with enterprise architects, delivery leads, DevOps engineers, and customer stakeholders to drive solution adoption, operational scalability, and long-term platform maintainability

Technical Experience & Tooling

Candidates should demonstrate hands-on architecture and design experience across the following domains:

Cloud Platforms: Deep expertise in both Google Cloud Platform (GCP) and Microsoft Azure enterprise data ecosystems.
Processing Engines: Apache Spark, PySpark, Databricks, Azure Synapse / Fabric Spark, GCP Dataproc.
Modern Migration & AI Automation: Building automated, agentic, or metadata-driven data migration pipelines, automated schema conversion, and data validation agents for on-prem to cloud lift-and-shift/re-platforming.
Messaging & Streaming: Apache Kafka, GCP Pub/Sub, Azure Event Hubs.
Orchestration: Apache Airflow, Google Cloud Composer, Azure Data Factory (ADF).
Analytical Storage & Warehouses: GCP BigQuery, Azure Synapse, Microsoft Fabric, Delta Lake/Parquet.
NoSQL / Cache Storage: MongoDB, Redis, Bigtable, Azure Cosmos DB.
Containerization & Deployment: Kubernetes (GKE and AKS), Cloud Run, Azure Container Apps.
API Frameworks: Python (FastAPI, Flask) for data service endpoints.
Databases & Querying: Advanced SQL validation and extraction from legacy/RDBMS systems (SQL Server, Oracle, PostgreSQL).
CI/CD & Version Control: Git, GitHub, GitLab CI/CD, Azure DevOps, and Infrastructure as Code (Terraform).

Required Qualifications & Skills

15+ years of progressive experience in Software Engineering, Data Engineering, and Enterprise Data Platform architecture.
5+ years architecting, migrating, and deploying production data platforms across GCP and/or Azure, with proven multi-cloud integration patterns.
Demonstrated track record leading on-premises/cloud to cloud modernization/migration initiatives, including data ingestion, transformation, automated data reconciliation, and cutover strategies.
Experience designing or integrating autonomous, agent-driven, or LLM-augmented data workflows (e.g., automated schema mapping, code conversion, data quality validation agents).
Proven hands-on experience designing and deploying Lakehouse platforms incorporating Medallion Architecture (Bronze/Silver/Gold) and Data Mesh / Data Product principles.
Deep knowledge of multi-cloud data governance, access management, and cataloging (Dataplex, Microsoft Purview, Unity Catalog).
Strong leadership capability in technical backlog management, agile scoping, sprint planning, and setting technical strategy for distributed teams.
Exceptional stakeholder communication skills: capability to articulate architectural trade-offs to C-level executives as smoothly as reviewing code with engineers.

Preferred Qualifications (Optional / Good to Have)

Multi-cloud certifications: Google Cloud Professional Data Engineer / Cloud Architect and/or Microsoft Certified: Azure Data Engineer Associate / Azure Solutions Architect Expert.
Hands-on knowledge of Snowflake and Databricks
Familiarity with AWS data services (Amazon S3, Redshift, Glue, EMR).
Experience with LangChain, LlamaIndex, or agentic framework integrations in data operations.

Education

Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, or equivalent practical experience.

Employment type

Full Time

Source details

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Pay: No pay amount identified in the saved description.
Location & working pattern: Hyderabad
We are seeking a Principal Data Architect to define and drive enterprise data architecture, modernization, and governance strategies across complex multi-cloud (Google Cloud Platform and Microsoft Azure) and hybrid environments. This role will design scalable, secure, and high-performing data platforms while partnering with business and engineering leaders to translate strategic objectives into impactful data solutions. A key focus of this role will be spearheading scalable, agent-driven data migration pipelines to transition legacy on-premises data assets to the cloud, setting the technical direction for engineering teams, and actively managing the customer's technical backlog and delivery roadmap.
Work authorization: No clear work-authorization passage found. Eligibility is unconfirmed.

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