Design, build, and maintain the production-grade data models and pipelines at the core of the CDM, covering user, transaction, and compliance data across retail and institutional product lines, owning the full lifecycle from architecture through deployment and maintenance.
Build data quality checks, data contracts, validation logic, and monitoring that protect the integrity of the foundations before issues propagate into regulatory reports or downstream workflows.
Partner with upstream engineering teams to fix data gaps and absorb product changes at the source, taking accountability for data issues anywhere in the stack rather than passing them downstream.
Support live regulatory exams, audits, and ad hoc regulator requests with accurate, timely data, balancing deadline-driven reactive work with long-term foundational projects.
Automate recurring manual workflows into scalable pipelines and self-serve tooling, building durable infrastructure that enables downstream teams and AI agents to answer their own questions.
Perform front-line on-call duties, triage pipeline incidents, contribute to root cause analysis, and maintain clear technical documentation and runbooks.
Requirements
2+ years of experience building and maintaining production data pipelines and data models, with daily proficiency in SQL, Python (scripting, automation, OOP), dbt, Airflow, and modern warehouse architecture (Snowflake or Databricks).
Demonstrated experience with data modeling patterns (star/snowflake schemas, OBTs, SCDs) and building certified or canonical data models that serve multiple downstream consumers.
Track record implementing data quality frameworks including data contracts, validation logic, reconciliation, and monitoring to catch issues before they reach downstream reports.
Experience supporting time-sensitive, high-stakes data needs (regulatory exams, audits, or financial reporting) while maintaining accuracy standards under pressure.
Proven ability to work independently, scoping and driving foundational data work end-to-end without waiting for direction, and converting tribal knowledge into durable, documented infrastructure.
Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.
Tech Stack
Airflow
Python
SQL
Benefits
Total compensation may include equity and bonus eligibility and benefits (including medical, dental, and vision).