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Databricks Architect

Avanade
Bengaluru, IND
Full-time
Senior · 12–15 years experience
Onsite
Discovered 2 weeks ago

Full Job Posting

Key Responsibilities

  • Lead presales solutioning for Azure Databricks opportunities — architecture proposals, effort estimation, solution demos, and client workshops — partnering with account and practice leadership on pursuits.
  • Design end-to-end Azure Databricks solution architectures, including Lakehouse (Delta Lake), Unity Catalog, Databricks SQL, Workflows, and integration with Power BI and the broader Azure ecosystem.
  • Define data platform strategy, target-state architecture, and migration roadmaps in collaboration with client stakeholders.
  • Architect modern data estate builds — data lake, data warehouse, and lakehouse patterns — including migrations from legacy platforms (on-prem warehouses, Synapse, Snowflake, or other cloud data platforms) to Azure Databricks.
  • Step into delivery as needed to provide technical leadership on active engagements: solution design, architecture governance, design reviews, performance and cost optimization, and production readiness.
  • Establish data architecture standards, security/governance models (Unity Catalog, data lineage, access control), and CI/CD/DevOps practices for Azure Databricks-based delivery.
  • Act as a trusted technical advisor to client stakeholders, translating business requirements into scalable, well-governed data architectures.
  • Mentor data engineers and architects on project teams; contribute to reusable accelerators and IP for the practice.
  • Stay current with the Databricks roadmap and broader Azure data platform ecosystem, bringing new capabilities into client proposals and solutions.
  • Required Skills & Experience
  • 12–15 years in data engineering/architecture roles, with the most recent chapter as a Data Architect actively implementing Azure Databricks-based solutions.
  • Hands-on architecture experience with Databricks: Lakehouse/Delta Lake, Unity Catalog, Databricks SQL, Workflows, and cluster/compute optimization.
  • Strong background in modern data estate design — data lake and data warehouse architecture patterns (medallion architecture, dimensional modeling, data mesh concepts where applicable).
  • Proven experience leading data migration programs — legacy on-prem or cloud platforms to Azure Databricks.
  • Solid grounding in the broader Azure data ecosystem: Azure Data Factory, Azure Synapse Analytics, ADLS Gen2, Azure SQL — with the ability to compare and position Databricks against these.
  • Strong SQL, data modeling, and performance tuning skills; strong hands-on Spark/PySpark expertise.
  • Experience with data governance and security frameworks — Unity Catalog, role-based access, data lineage, and compliance considerations in enterprise environments.
  • Demonstrated experience in presales solutioning — proposals, estimation, and client-facing workshops — alongside hands-on delivery capability.
  • Experience mentoring teams and owning technical delivery quality on medium-to-large engagements.
  • Preferred / Good to Have
  • At least 3 years of hands-on experience specifically on Azure Databricks.
  • Databricks certifications: Databricks Certified Data Engineer Professional, Databricks Certified Machine Learning Professional, or Databricks Solutions Architect.
  • Exposure to AI/ML integration on Databricks (MLflow, Databricks Model Serving) or broader GenAI/agentic data use cases.
  • Experience working across multiple industry domains (manufacturing, BFSI, consumer goods, energy, healthcare, or others).
  • Experience working in a global delivery / SI environment with distributed teams across geographies.
  • Prior experience contributing to reusable accelerators, frameworks, or practice-building initiatives.

Qualifications

  • 12–15 years in data engineering/architecture roles, with the most recent chapter as a Data Architect actively implementing Azure Databricks-based solutions.
  • Experience with data governance and security frameworks — Unity Catalog, role-based access, data lineage, and compliance considerations in enterprise environments.

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