Sr. Data Analytics Engineer - Databricks

End-to-end impact Build full-stack analytics from lake house pipelines to real-time dashboards.

Fail-safe engineering TDD, CI/CD, DAX optimization, Unity Catalog, cluster tuning.

Modern stack Databricks, PySpark, Delta Lake, Power BI, Airflow.

Mentorship culture Lead code reviews, share best practices, grow as a domain expert.

Mission-critical context Help enterprises migrate legacy analytics into cloud-native, governed platforms.

Compliance-first mindset Work in HIPAA-aligned environments where precision matters.

Build scalable pipelines using SQL, PySpark, Delta Live Tables on Databricks.

Orchestrate workflows with Databricks Workflows or Airflow; implement SLA-backed retries and alerting.

Design dimensional models (star/snowflake) with Unity Catalog and Great Expectations validation.

Deliver robust Power BI solutions dashboards, semantic layers, paginated reports, DAX.

Migrate legacy SSRS reports to Power BI with zero loss of logic or governance.

Optimize compute and cost through cache tuning, partitioning, and capacity monitoring.

Document everything from pipeline logic to RLS rulesin Git-controlled formats.

Collaborate cross-functionally to convert product analytics needs into resilient BI assets.

Champion mentorship by reviewing notebooks, dashboards, and sharing platform standards.

5+ years in analytics engineering, with 3+ in production Databricks/Spark contexts.

Advanced SQL (incl. windowing), expert PySpark , Delta Lake , Unity Catalog .

Power BI mastery DAX optimization, security rules, paginated reports.

SSRS-to-Power BI migration experience (RDL logic replication).

Strong Git, CI/CD familiarity, and cloud platform know-how (Azure/AWS).

Communication skills to bridge technical and business audiences.

Databricks Data Engineer Associate cert.

Streaming pipeline experience (Kafka, Structured Streaming).

dbt , Great Expectations , or similar data quality frameworks.

BI diversityexperience with Tableau, Looker, or similar platforms.