Lead Data Engineer building ETL, validation, and scalable data pipelines for AirOps' AI marketing platform. Ensure reliable analytics and productionize ML models across cloud environments.
Responsibilities
Design, build, and maintain scalable ETL pipelines for ingesting and transforming large volumes of data
Implement automated data validation, monitoring, and alerting to ensure quality and reliability
Integrate diverse internal and external data sources into unified, queryable datasets
Optimize storage and query performance for analytical workloads
Collaborate with data scientists to productionize ML models and ensure they run reliably at scale
Work with product and engineering teams to meet data needs for new features and insights
Maintain cost efficiency and operational excellence in cloud environments
Build robust ingestion, cleanup, and integration pipelines to ensure accurate, reliable data ready for analysis
Requirements
4+ years of experience in data engineering, ideally in AI, SaaS, or data-intensive products
Strong fluency in Python and SQL
Experience with modern data modeling tools such as dbt
Experience with data warehouses and OLAP databases (e.g., Redshift, Snowflake, BigQuery, ClickHouse)
Proven ability to design and maintain production-grade data pipelines in cloud environments (AWS, GCP, or similar)
Familiarity with orchestration frameworks (Airflow, Dagster, Prefect)
Comfort operating in fast-paced, ambiguous environments where you ship quickly and iterate
Benefits
Equity in a fast-growing startup
Competitive benefits package tailored to your location
Flexible time off policy
Generous parental leave
A fun-loving and (just a bit) nerdy team that loves to move fast!
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