AirOps helps brands get found and stay found in the AI era. As the first end-to-end content engineering platform, we give marketing teams the systems to win visibility across traditional and AI search with one durable advantage: quality.
Thousands of marketers use AirOps to see how their brand shows up across the new discovery landscape, prioritize the highest-impact opportunities, and create accurate, on-brand content that earns citations from AI platforms and trust from humans. We are building the platform and profession that will equip a million marketers to lead the next chapter of marketing, where creativity and intelligent systems work together and quality becomes the strategy that lasts.
AirOps is backed by Greylock, Unusual Ventures, Wing VC, Founder Collective, XFund, Village Global, Alt Capital, and more than a dozen top marketing leaders, with hubs in San Francisco, New York, and Montevideo.
About the RoleAs 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 ResponsibilitiesData 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.
5+ years in data engineering with 2+ years leading projects
Expert SQL and Python with deep experience building production pipelines at scale
Hands-on with dbt and a workflow manager such as Airflow or Prefect
Strong background in dimensional and event-driven modeling and a company-wide metrics layer
Experience with Snowflake or BigQuery, plus Postgres for transactional use cases
Track record building data products for analytics and customer reporting
Cloud experience on AWS or GCP and infrastructure as code such as Terraform
Domain experience in SEO, content analytics, or growth experimentation is a plus
Clear communicator with a bias for action, curiosity, and a high bar for quality
Extreme Ownership
Quality
Curiosity and Play
Make Our Customers Heroes
Respectful Candor
Equity in a fast-growing startup
Competitive benefits package tailored to your location
Flexible time off policy
Parental Leave
A fun-loving and (just a bit) nerdy team that loves to move fast!
Top Skills
What We Do
Build your AI growth engine.
AirOps lets you easily build and scale AI workflows to crush your growth targets. Build with 40+ AI models, retrieval, and data sources or launch one of our proven playbooks.
