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

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, Git…