AirOps
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. What You’ll Own
We’re hiring a Data Engineer to design and maintain the high-scale data infrastructure that powers the AirOps platform. You will build robust ingestion, cleanup, and integration pipelines, ensuring that the data our customers rely on for brand visibility insights is accurate, reliable, and ready for analysis. Responsibilities
Design, build, and maintain scalable ETL/ELT 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
Your Experience
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
About You
You love building systems that make data accurate, reliable, and accessible at scale
You think in terms of automation and scalability, not manual workarounds
You collaborate well with data scientists, product managers, and engineers
You enjoy working with large, complex datasets and solving performance challenges
You take pride in operational excellence and care about the quality of the data you deliver
Our Guiding Principles
Extreme Ownership
Quality
Curiosity and Play
Make Our Customers Heroes
Respectful Candor
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!
#J-18808-Ljbffr
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. What You’ll Own
We’re hiring a Data Engineer to design and maintain the high-scale data infrastructure that powers the AirOps platform. You will build robust ingestion, cleanup, and integration pipelines, ensuring that the data our customers rely on for brand visibility insights is accurate, reliable, and ready for analysis. Responsibilities
Design, build, and maintain scalable ETL/ELT 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
Your Experience
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
About You
You love building systems that make data accurate, reliable, and accessible at scale
You think in terms of automation and scalability, not manual workarounds
You collaborate well with data scientists, product managers, and engineers
You enjoy working with large, complex datasets and solving performance challenges
You take pride in operational excellence and care about the quality of the data you deliver
Our Guiding Principles
Extreme Ownership
Quality
Curiosity and Play
Make Our Customers Heroes
Respectful Candor
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!
#J-18808-Ljbffr