We are seeking a Senior Data Engineer with strong hands-on expertise in Databricks, PySpark, and cloud-based data platforms to support the development, migration, and optimization of our enterprise data platform within the investment domain.

This role will focus on building and maintaining scalable data pipelines and lakehouse data models that support investment analytics, portfolio management, risk analysis, and trading data workflows.

The successful candidate will work closely with data engineers, quantitative analysts, and investment stakeholders to deliver high-quality, reliable, and performant data solutions. Experience with financial datasets such as market data, portfolio holdings, transactions, pricing data, and risk metrics is highly valuable.

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.