Top Fintech Data Engineering Jobs in New York City, NY

As a Data Analytics Engineer, you'll design clean datasets in Snowflake for AI tooling, ensuring teams access reliable data for decision-making.

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.

The Lead Data Engineer will design and develop technical solutions, collaborate with agile teams, and implement cloud-based data solutions, focusing on real-time data applications and big data technologies.

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.

The Finance Systems Architect is responsible for designing and optimizing finance technology solutions, ensuring alignment with business needs, and leading initiatives for system integrations and process automation.

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.

The Senior Analytics Engineer will design data models, build data pipelines, and support analytics needs across teams, enhancing data infrastructure.

The Data Platform Engineer will architect and build a foundational data platform for AI, managing ingestion systems, data quality, and mentorship while working with diverse data types and technologies.