Data Engineer
Job Summary
DOJO AI is seeking a Data Engineer to own and scale their mission-critical data infrastructure. You will manage end-to-end data pipelines, including ingestion, transformation, and quality assurance, to power AI agents and product features. The role requires building a robust, graph-based semantic layer that synthesizes unstructured marketing data into actionable intelligence.
Responsibilities
- End-to-end data platform ownership
- Build graph-based semantic layers
- Enforce data quality and schema validation
- Collaborate with AI and product teams
- Optimize data infrastructure for AI systems
Required Skills
- Python
- Data Engineering Patterns
- Modern Data Stack (Dagster, Polars, etc.)
- Infrastructure for AI/MLOps
- Streaming Architectures
Job Details
Responsibilities:
- Own the data platform end-to-end - ingestion from diverse sources, transformation, quality assurance, and delivery at scale, making deliberate infrastructure choices that compound over time.
- Build and maintain the graph-based semantic layer that bridges unstructured marketing signals with structured, queryable data that is continuously enriched and updated.
- Establish and enforce data quality standards - schema validation, anomaly detection, and monitoring that catches problems before customers do.
- Work directly with the founders, AI engineers, and product teams to ensure the data layer enables fast iteration on new features and agent capabilities.
- Close the AI-data loop - keep data fresh and accurate so our agents learn from the best signals, and use AI at the core of the data infrastructure itself.
- Help shape our engineering culture and raise the bar as the team grows - through code review, architectural decisions, and how we work together.
Requirements:
- Deep experience building and operating production data infrastructure - pipelines, storage, quality systems, and the tooling around them.
- Experience with modern data engineering patterns - data mesh, data vault, data quality, and data governance - not classic ETL and BI warehouse architectures.
- Strong proficiency in Python and modern data tools (e.g. Dagster, Polars, DuckDB, Apache Iceberg, Apache Arrow, ClickHouse, Kafka).
- An eye for data quality and scale as a first-class engineering problem, not an afterthought.
- Strong foundations in CS or Engineering.
- Experience with data infrastructure for AI systems - evaluation pipelines, data flywheels, or MLOps - is a strong plus.
- Experience with streaming architectures and real-time data pipelines is a plus.