Senior Databricks Data Engineer

Key Responsibilities:

Develop and maintain scalable data pipelines and transformation workflows using Databricks, PySpark, and SQL.

Support the migration of datasets, pipelines, and transformation logic from Palantir Foundry to Databricks Delta Lake.

Work with investment and financial datasets, including market data, portfolio positions, transactions, pricing data, and risk metrics.

Build and maintain ETL/ELT pipelines that enable data availability for investment analytics, reporting, and portfolio management systems.

Implement data validation, reconciliation, and quality frameworks to ensure financial data accuracy and consistency.

Optimize Spark jobs, cluster configurations, and storage formats to improve performance and cost efficiency.

Maintain data lineage, documentation, and governance practices to meet financial industry standards.

Support monitoring, troubleshooting, and performance tuning of production data pipelines.

Mandatory Skills:

Advanced Databricks Experience. Deep knowledge of Databricks architecture, Delta Lake, job orchestration, cluster management, and performance tuning.

Experience working with investment data, such as market data, portfolio holdings, transactions, pricing data, risk metrics, and financial instruments

Expert-Level PySpark & Python Skills. Strong ability to design, optimize, and refactor distributed data processing workflows.

Advanced SQL & Data Modelling Expertise. Experience in dimensional modeling, lakehouse architecture patterns, and query optimization.

Cloud Platform Experience (Azure preferred). Hands-on experience deploying and managing data platforms in cloud environments, including storage, security, and networking considerations.

Strong Hands-on Expertise in Palantir Foundry. Proven experience with Foundry pipelines, ontologies, data lineage, transformations, and platform governance.

Proven Migration Experience from Palantir / to Databricks. Demonstrated experience leading or executing platform migrations, including pipeline conversion, data model redesign, and production cutover.

Familiarity with Dynatrace or Datadog for system observability and monitoring.

Databricks certification, cloud certifications (Azure/AWS), or enterprise data architecture certifications.