Lead Data Engineer

About AirOps

Today thousands of leading brands and agencies use AirOps to win the battle for attention with content that both humans and agents love.

We're building the platform and profession that will empower a million marketers to become modern leaders - not spectators - as AI reshapes how brands reach their audiences.

We're backed by awesome investors, including Unusual Ventures, Wing VC, Founder Collective, XFund, Village Global, and Alt Capital, and we're building a world-class team with in-person hubs in San Francisco, New York, and Montevideo, Uruguay.

About the Role

As Lead Data Engineer, you will own and scale the data platform that powers AirOps insights on AI search visibility and content performance. You will set technical direction, write production code, and build a small, high-output team that turns raw web, content, and AI agent data into trustworthy datasets.

Your work will drive customer-facing analytics and product features while giving our content and growth teams a clear path from strategy to execution. You value extreme ownership, sweat the details on data quality, and love partnering across functions to ship fast without losing rigor.

Key Responsibilities

• 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.

Qualifications

Our Guiding Principles

Benefits