Top Fintech Data Engineering Jobs

The role oversees multi-asset trading, manages index operations, develops AI infrastructure, and maintains portfolio analytics, requiring a strong technical skill set.

The role involves implementing mortgage prepayment models into a financial analytics library using C++, requiring collaboration across teams and adherence to software engineering practices.

Lead the strategy and operations for database platforms, ensuring availability, performance, and security. Collaborate with engineering and operations on Microsoft SQL Server and Azure capabilities while promoting automation and AI-enabled operations.

Lead Data Engineer responsible for data architecture, building scalable data pipelines, ensuring data quality, providing technical leadership, and collaborating across teams.

Lead the architecture and design of data and analytics solutions, focusing on ETL, data warehousing, and cloud technologies while collaborating with global teams to optimize data processes.

The Senior Data Engineer II will design and maintain scalable data pipelines, collaborate with teams on machine learning workflows, and ensure data accessibility across systems.

Lead development and ownership of BigQuery data assets, pipelines, and observability. Build and optimize dbt models, CDC/file/streaming ingestion, and streaming replication. Partner with engineering and business teams to enable self-service analytics, advanced analytics (LTV, churn, attribution), and reliable real-time data products.

The Data Engineer will design and build scalable data systems, support AI/ML integration, and collaborate across teams for the development of Canoe's Alts Data Platform.

Curate, cleanse, validate, and maintain large US equity and options datasets; integrate fundamental and alternative data; build data pipelines for research and trading; perform statistical and ML analysis to identify market patterns; document data provenance and collaborate with engineers and traders to deploy production-grade models.

Lead the ML Data Enablement team to define strategy and execution for accelerating third-party and internal data onboarding, enforce data quality and SLAs, build metadata and lineage standards, and deliver scalable data infrastructure and workflows to reduce time-to-production for ML models.