Design and build the data platform: ingestion, transformation, storage, and serving
Integrate many heterogeneous data sources with varying formats, quality levels, and update frequencies
Own the data quality framework: validation, lineage tracking, monitoring, and alerting
Leverage AI coding agents to accelerate development and infrastructure management
Document architecture decisions in lightweight ADRs (architecture decision records)
Document data source mappings and transformation logic alongside the code
Generate insight and visualization for non-technical users of data.
Ability to quickly assimilate and understand industry data nuance
Senior+ software engineer with strong architectural judgment
Demonstrated experience integrating messy, real-world data from multiple sources — not just clean API-to-API pipelines
Familiarity with data quality tooling (e.g., Great Expectations, dbt tests, Soda, or equivalent)
Strong experience using AI coding agents to deliver production-quality projects and extend them over time
Comfort operating as the sole technical builder, at least in the initial phase
A strong cultural fit marked by ownership, humility, initiative, and collaboration
A self-starting mindset with the ability to move work forward without constant direction
A bias toward action, learning, and improvement in fast-paced environments
Clear and professional communication skills, both verbal and written
Strong organizational skills and the ability to manage multiple priorities effectively

Data & AI Systems Engineer

Key skills