The role

We're hiring a Data Engineer to own the pipelines, models, and platforms behind trustworthy analytics and AI. You'll design and run ingestion, transforms, orchestration, quality, and the shared definitions that analysts, agents, and go-to-market tools rely on—partnering with Product, Engineering, Analytics, Marketing, Sales, and Customer Success.

We use AI agents for monitoring, troubleshooting, documentation, and first-pass fixes so you can focus on architecture, reliability, and hard design choices. You'll treat the platform as people and agents working in a loop, not a ticket queue.

What you'll own

• Own the warehouse, transforms, orchestration, and serving layer (dashboards, reverse ETL, agents).
• Build reliable pipelines from product events, CRM, marketing, finance, support, and other sources—with contracts, retries, and clear visibility.
• Ship reusable, tested models and metrics; run quality as operations (tests, anomalies, lineage, SLAs, incidents).
• Partner with Engineering on events, tracking plans, and data contracts; govern access and PII with docs people use.
• Build the agent layer: health monitoring, failed-job summaries, cost and performance flags, draft models/tests/docs, schema-change alerts, weekly health reports, and suggested fixes for repeat incidents—you own its quality and adoption.
• Run platform rhythm: reviews, retros, SLA reporting, and prioritization with Product, Analytics, and GTM; clear ownership, tests, docs, and rollback for major data products.

You deliver as a high-leverage individual contributor—outcomes end to end, no direct reports—and influence through expertise, systems, and steady delivery.

Who thrives here

Success looks like a trusted platform with SLAs and fast recovery; safe instrumentation as product changes; agents that shorten signal-to-fix; less manual work and better cost as volume grows; and quality that scales without growing headcount.

We're looking for 5+ years hands-on production pipelines and warehouse models. Strong SQL; cloud warehouse and dbt-style transforms; orchestration (Airflow, Dagster, Prefect, or similar); Python for automation. You design for observability, testing, incidents, and cost; think in systems; partner well across functions; and want to document and automate—not manage people.