Data Engineer-Financial Services

NO H1S OR 3RD PARTIES

THIS ROLE WILL BE ONSITE 3 DAYS PER WEEK IN NYC

• Develop and maintain data models in dbt, implementing tests, documentation, and lineage tracking as standard practice.
• Translate business logic from legacy SQL Server systems into clean, tested dbt models as part of ongoing platform modernization.
• Build and optimize data pipelines that ingest, transform, and deliver investment, portfolio, and client data across the firm.
• Build and optimize analytical datasets in Snowflake to support reporting and business intelligence.
• Write and maintain Airflow DAGs, Python, SQL for scheduled and event-driven workflows, following CI/CD practices for deployment.
• Collaborate across engineering, analytics, and business teams – integrating into a firm where everyone contributes beyond their lane.
• Identify manual or fragile processes and build automated, sustainable replacements. Own problems end-to-end.
• Implement monitoring and alerting (Datadog) to catch pipeline failures and data quality issues before they reach stakeholders.
• Participate in code reviews and contribute to team engineering standards

• 2-4+ years of experience in data engineering roles.
• Required: Strong Python and SQL. Comfortable writing production code, not just notebooks.
• Required: Experience with Snowflake or similar cloud warehouse. Working knowledge of SQL Server - able to read and reverse-engineer stored procedures, views, and legacy logic. Hands-on Airflow/Python ETL tooling preferred.
• Required: Prior experience working with financial services data. Candidates must demonstrate meaningful exposure to relevant data domains such as securities identifiers (tickers, FIGI, CUSIPs, ISINs), client portfolios, positions and holdings, performance reporting, or fund operations. Experience at a wealth manager, asset manager, hedge fund, or market/alternative data provider strongly preferred.
• Comfortable working autonomously with minimal direction. Proactive problem-solver who acts on gaps without waiting for assignment.
• Experience with dimensional modeling and data warehouse design patterns.
• Able to explain technical tradeoffs to non-technical stakeholders clearly and without jargon.
• Preferred: Experience with the following technologies: dbt, Docker, Terraform, Azure, GitHub Actions, Datadog.