Senior Software Engineer - Backend, Data Products - MarTech/AdTech

As a Senior Software Engineer on Backend, Data Products, you build the product itself - the composable, self-correcting pipeline capabilities customers actually use. You develop Reactors (the Observe / Match / Act / Compose primitives at the heart of the platform), the connectors and tooling that bring messy real-world data in, the validation that keeps it trustworthy, and the backend/GraphQL surface that exposes it. This is applied backend engineering: turning a hard product vision into resilient, shippable features.

What You’ll Do

• Build and extend Reactors - the Observe / Match / Act / Compose primitives that make pipelines composable and self-correcting.
• Develop connectors, ingestion, and transformation tooling for diverse, messy marketing data sources, including schema evolution.
• Build data validation and quality into the pipeline (e.g., Great Expectations-style contracts) so correctness is enforced, not hoped for.
• Design and ship the backend / GraphQL surface that exposes pipeline capabilities to internal teams and customers.
• Orchestrate multi-step pipeline work with workflow engines (Temporal) and event-driven dispatch (NATS/JetStream).
• Collaborate in a sprint-based cycle with product and science to turn analytical workloads into production features.

Software Engineering, Adtech, Machine Tool, Software Engineering, GraphQL, Product Development, Transformation Tools, Marketing, Data Quality, Analysis Skills, Engineering fundamentals

• 5+ years writing production software, with deep Python (3.10+, async/await, type hints; pandas, SQLAlchemy, Pydantic).
• Built resilient, observable ETL/ELT pipelines that ingest, transform, and validate data at scale.
• API/backend design: GraphQL and/or REST contracts, versioning, and ergonomics other engineers actually enjoy using.
• PostgreSQL depth - schema design, query optimization; comfortable with both ORM and raw SQL.

Systems & tooling

• Event-driven and workflow-orchestrated systems (Temporal, NATS / Kafka, or similar).
• Data validation / quality frameworks, and a habit of designing for correctness and debuggability.

Nice to have

• Plugin / extension development (Apache NiFi or similar) and fluency with AI coding assistants as part of your workflow.
• Performance-sensitive work in a systems language (Rust, C++); graph / lineage modeling; connector ecosystems.