AirOps - Lead Data Engineer

• Data platform ownership: design, build, and operate batch and streaming pipelines that ingest data from crawlers, partner APIs, product analytics, and CRM. • Core modeling: define and maintain company-wide models for content entities, search queries, rankings, AI agent answers, engagement, and revenue attribution. • Orchestration and CI: implement workflow management with Airflow or Prefect, dbt-based transformations, version control, and automated testing. • Data quality and observability: set SLAs, add tests and data contracts, monitor lineage and freshness, and lead root cause analysis. • Warehouse and storage: run Snowflake or BigQuery and Postgres with strong performance, cost management, and partitioning strategies. • Semantic layer and metrics: deliver clear, documented metrics datasets that power dashboards, experiments, and product activation. • Product and customer impact: partner with Product and Customer teams to define tracking plans and measure content impact across on-site and off-site channels. • Tooling and vendors: evaluate, select, and integrate the right tools for ingestion, enrichment, observability, and reverse ETL. • Team leadership: hire, mentor, and level up data and analytics engineers; establish code standards, review practices, and runbooks.