Staff Data Engineer

What You'll Do

• Architect the platform. Set our warehouse/lakehouse direction and stand up the data lake and layered architecture that turns our raw system of record into trustworthy, queryable, intelligence-ready data.
• Build the pipelines. Design and run batch and streaming pipelines that move data reliably out of our production systems - CDC, ELT, and real-time where it matters.
• Model the data. Define the canonical datasets and models the whole company depends on, getting the grain, semantics, and contracts right.
• Own reliability and accuracy. This is financial data, so correctness is non-negotiable. You'll own data quality, observability, integrity checks, and the testing and monitoring that let us trust it.
• Build for a regulated environment. Design in role-based access, masking, lineage, and auditability from day one, and keep sensitive financial data out of places it doesn't belong.
• Enable AI/ML and analytics. Build the feature pipelines and trustworthy data foundation our intelligence work relies on, moving us from systems of record toward systems of intelligence and action.
• Set the standard. Establish the practices, tooling, and CI/CD for data that the future team inherits.

What We're Looking For

• 8+ years building production data systems, with a track record of owning architecture and seeing big decisions through to production.
• Expert SQL and strong Python.
• Deep experience in at least one modern lakehouse/warehouse ecosystem (e.g., Snowflake with dbt and Fivetran, or Databricks with Spark, Delta Lake, and Unity Catalog).
• Strong data modeling skills (dimensional, normalized, or Data Vault).
• Experience with pipeline orchestration (Airflow, Dagster, Prefect, or equivalent) and large-scale processing (such as Spark).
• Production experience on a major cloud (AWS, GCP, or Azure), including security and cost patterns.
• Experience working with sensitive or regulated data (access controls, encryption, governance).

Nice to Haves

• Payments, fintech, or other regulated-domain experience, including familiarity with PCI DSS and tokenization/vaulting patterns.
• Streaming infrastructure (Kafka, Kinesis, Flink).
• Data governance, lineage, and observability tooling (Unity Catalog, Snowflake Horizon, Monte Carlo, Great Expectations, OpenLineage).
• Experience supporting ML/AI workloads (feature stores, training/inference pipelines, MLflow).
• Interest in growing into people leadership.

Compensation & Benefits

• Competitive salary
• Stock options with the potential to unlock more equity as we grow
• Flexible PTO and paid parental leave
• Medical, dental, & vision insurance
• 401K, HSA, pre-tax savings programs