First Derivative is driven by people, data, and technology, unlocking the value of insight, hindsight, and foresight to drive organizations forward. Counting many of the world’s leading investment banks as clients, we help our clients navigate the data-driven, digital revolution that is transforming the financial services sector. Our global teams span across 15 offices serving clients across EMEA, North America and APAC.
As an EPAM Systems, Inc. (NYSE: EPAM) company, a leading global provider of digital platform engineering and development services, we deliver advanced financial services solutions by empowering operational insights, driving innovation, and enabling more effective risk management in an increasingly data-centric world. Together with EPAM, we combine deep industry expertise with cutting-edge technology to help clients stay ahead in a rapidly evolving financial landscape, offering comprehensive solutions that drive business transformation and sustainable growth.
We are seeking a highly skilled Data Engineer with strong SQL capabilities and hands-on experience with AWS Glue or equivalent Spark-based tools (e.g., Databricks).
You will be a key contributor in our Data Modernization initiative, helping to design and build scalable data processing pipelines that support our AWS-based data lake. The role involves working with large-scale datasets, optimizing for performance through techniques like partitioning, and delivering clean, reliable data to downstream consumers.

REQUIREMENTS

• Excellent SQL skills — advanced experience writing performant queries using CTEs, procedures, and views
• Hands-on experience with AWS Glue (Spark-based ETL), or similar platforms like Apache Spark or Databricks
• Strong understanding of partitioning techniques for large-scale datasets in both databases and data lake environments (e.g., Glue, Athena, Spark)
• Familiarity with cloud data lake architectures and AWS data ecosystem (S3, Athena, Glue, etc.)
• Comfortable working with large volumes of data and optimizing jobs for performance and cost
• Experience in a collaborative environment, with the ability to communicate effectively across technical and non-technical teams
• Financial services experience is a plus, especially familiarity with reference, counterparty, or instrument data

• Develop and maintain robust ETL pipelines using AWS Glue (Apache Spark) or Databricks
• Write complex SQL queries, including Common Table Expressions (CTEs), stored procedures, and views, for data transformation and analysis
• Design and implement effective partitioning strategies in Glue, Athena, and other AWS-native tools to optimize performance and cost
• Ingest, clean, and transform structured and semi-structured data from multiple sources into the AWS data lake
• Collaborate with stakeholders to understand data requirements and deliver well-structured, high-quality datasets
• Troubleshoot performance issues in data pipelines and contribute to tuning and optimization
• Support data governance, lineage, and monitoring initiatives to ensure data quality and reliability