When the Wall Street Journal, Bloomberg, or the New York Times reports on the streaming wars, the numbers trace back to Antenna. We're the market standard for the Subscription Economy: the pipelines and data models you own here process the signal that the biggest brands in media and entertainment rely on to decide what to make, what to charge, and how to grow. The systems you build aren't internal tooling that quietly does its job, they get cited, scrutinized, and trusted at the highest levels of the industry, which means the engineering bar here is real: accuracy, scalability, and reliability aren't nice-to-haves, they're the product.

We are looking for a Senior Data Engineer to join our team to ensure the accuracy and scalability of Subscriber Metrics, one of Antenna's core data products. In this role, you will partner with our Data Operations and Data Science teams to tackle complex data engineering problems and integrate data models. You will own the reliability of our data delivery and serve as the subject matter expert for our delivery pipeline. The incoming Senior Data Engineer will join a collaborative team that requires a technical, driven team member who brings a solid programming foundation, cloud platform experience, and a passion for automation.

You will report to the VP, Engineering. Antenna is a remote-first company, and we are looking for candidates who can work during US business hours. Preferably, you should be based in a US time zone (Eastern, Central, Mountain, or Pacific).

What You’ll Do

• Build, orchestrate, and maintain data pipelines on our GCP stack, using BigQuery and dbt for modeling, PySpark and Parquet for large-scale processing, and Dagster for orchestration.
• Develop a deep understanding of Antenna's core data models in order to collaborate effectively with Data Science and Data Ops
• Partner with the Data Science and Data Ops teams to maintain datasets that are trusted, well-understood, and enable self-service
• Work with a wide range of cross-functional stakeholders to derive and define requirements
• Document, simplify, and explain complex problems to different types of audiences
• Establish best practices for the development of specialized datasets for analytics and modeling

Who You Are

• 5+ years in software engineering, with at least 3 years focused specifically on data infrastructure or platform engineering
• You've built production pipelines in a modern cloud warehouse (BigQuery, Snowflake, or Databricks) with dbt, SQL, or Spark and an orchestrator like Dagster or Airflow
• You have expertise with SQL and at least one general-purpose programming language, preferably Python, for building pipelines, custom integrations, automation scripts, and occasional web services
• You independently navigate complex, unfamiliar systems to identify and fix difficult problems
• You proactively, consistently, and effectively communicate with cross-functional teammates and stakeholders
• You care about and understand the testability of data systems
• You use AI tools and automation to genuinely improve your work, and you're pragmatic rather than reliant on them
• Bonus: You design and implement backend web services
• Bonus: You manage cloud resources (GCP or AWS) using infrastructure as code tools like Terraform, Pulumi, or CloudFormation
• Bonus: You design maintainable systems to solve challenging customer problems
• Bonus: You bring industry experience with receipt, transaction, or viewership data
• Bonus: You have experience with data architecture and governance
• Advanced English proficiency (B2-C1) Please submit a resume in English language in the application.

Our Stack

• Python, including testing, type checking, and modern dependency management
• SQL, primarily BigQuery and dbt
• Google Cloud, especially BigQuery, Cloud Run, GCS, and Dataproc
• Terraform, Docker, and CI/CD
• PySpark and Parquet-based data processing
• Dagster or a comparable orchestration framework
• FastAPI or a comparable Python web framework
• Applied ML using scikit-learn or PyTorch
• LLM applications using Gemini or another major model provider, with structured outputs and agent/workflow frameworks such as LangGraph
• AI coding tools such as Claude Code, Cursor, or Code