Key Responsibilities:

- Customer Engagement: Partner with sales and delivery teams to lead technical conversations, workshops, and proof-of-concepts with enterprise clients.

- Solution Design: Architect end-to-end Databricks Lakehouse solutions covering ingestion, transformation, governance, ML, and BI aligned to Medallion Architecture principles.

- Modernization Programs: Drive migrations from legacy platforms (Teradata, Netezza, Oracle, SAS, Hadoop, Snowflake, Synapse) to Databricks with clear ROI/TCO articulation.

- Advanced Feature Adoption: Guide customers in using Lakeflow Declarative Pipelines (DLT), Lakeflow Connect, Unity Catalog, Serverless Compute, MLflow, and Mosaic AI.

- Technical Leadership: Produce reference architectures, migration roadmaps, and executive-ready deliverables; act as escalation point for complex technical challenges.

- Community Contribution: Promote and contribute to open-source projects like Apache Spark, Delta Lake, and MLflow.

Core Skills & Requirements:

- Experience: 8 to 14 years in data engineering, architecture, or consulting roles.

- Big Data Expertise: Apache Spark, Hadoop, Cassandra, PySpark, SQL, ML/AI workloads.

- Cloud Platforms: Proven solutions built on AWS, Azure, or GCP.

- Programming: Strong coding in Python, R, Java, Scala.

- Consulting Skills: Excellent client-facing communication, ability to translate business needs into technical blueprints.

- Certifications: Databricks certifications are highly valued.

Role Impact:

- Shape enterprise data modernization journeys by enabling scalable, governed, and AI-ready architectures.

- Act as a trusted advisor to CDOs, Data Architects, and Engineering leaders.

- Influence Databricks product roadmap by providing field insights.

- Ensure customer success in mission-critical environments through architecture deep-dives, PoCs, and cost-performance benchmarking.