Sobre esta vaga de Databricks Architect na Accenture
Role Summary
Avanade India’s Data & AI practice is looking for a Databricks Architect to drive presales solutioning and technical architecture for modern data estate, data lake, data warehouse, and data migration engagements built on Azure Databricks, stepping into delivery leadership as engagements require. This is a client-facing role for someone who enjoys shaping opportunities as much as solving hard architecture problems — from client workshops and proposal-stage solutioning through to guiding implementation teams when needed.
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.
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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.
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