Analytics Engineer

What you'll do

• Own end-to-end development of production-grade data models and pipelines at the core of the CDM, covering user, transaction, and compliance data across retail and institutional product lines, from architecture through deployment and maintenance.
• Build data quality checks, data contracts, validation logic, and monitoring that protect data integrity before issues propagate to downstream reports or 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 time-sensitive reactive work with long-term foundational projects.
• Automate recurring manual workflows into scalable pipelines and self-serve tooling, enabling downstream teams and AI agents to answer their own questions.

Required Skills and Experience

• 2+ years of experience building and maintaining production data pipelines and data models, with strong proficiency in SQL, Python, dbt, and a modern warehouse platform (Snowflake, Databricks, or similar).
• 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) where accuracy and timeliness are non-negotiable.
• Demonstrated ability to turn tribal knowledge and recurring manual workflows 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.