Location: Madrid, Spain (Remote — with regular in-person collaboration at a local WeWork alongside our Madrid tech team)

About MediaRadar

MediaRadar, equips marketing, sales and analytics leaders with the intelligence they need to stay ahead. Our platform delivers always-on, AI-enabled Creative, Competitive, Commercial and Market Intelligence—spanning ad strategy, media spend, creative assets and brand messaging across 30+ media channels and five million brands.

With deep insights into more than 35 million ad and campaign assets and $280 billion in media spend, MediaRadar provides a single, interoperable source of truth that plugs seamlessly into enterprise analytics and AI systems. The result: faster, cleaner and more actionable intelligence that drives competitive advantage.

About The Role

We are looking for a Data Engineering Manager to join our Madrid-based team and take ownership of our data delivery lifecycle — driving the end-to-end delivery of complex data projects while managing and mentoring a growing team of data engineers. This is a player‑coach role: at least for now, you will remain deeply hands‑on — writing code, designing schemas, and building proofs‑of‑concept alongside your team — rather than stepping into a purely managerial capacity.

We are deliberately moving away from a locked‑in, vendor‑heavy stack (e.g., Azure Databricks) toward a versátil, largely open-source architecture that keeps our options open. You will be in the engine room of that re‑architecture — writing code, designing schemas, tuning queries, and helping prove out tools such as ClickHouse, dbt,



and open-source orchestration frameworks before we adopt them at scale — while also mentoring your team to do the same and championing AI‑assisted development practices (e.g., GitHub Copilot, Cursor, Claude) across the group.

Requirements

- 6-8+ years in data engineering / ETL as a hands‑on engineer, with some experience leading, mentoring, or informally guiding other engineers — formal people‑management experience is a plus but not a requirement, given the hands‑on nature of this role today
- Strong SQL and RDBMS skills with solid, hands‑on experience in SQL Server and Postgres — schema design, performance tuning, and complex query optimization
- Hands‑on experience with tools such as dbt, ClickHouse, and open‑source pipeline / orchestration tools (e.g., Airflow, Dagster), with the judgment to choose the right tool for the job
- Strong proficiency in Python (or a similar language) for building and automating data pipelines
- Hands‑on experience using AI coding assistants and effective prompting techniques (e.g., GitHub Copilot, Cursor, Claude), with the judgment to verify, test, and refine AI-generated code and queries — and the ability to set standards for how a team uses these tools
- Hands‑on experience with a major cloud platform; AWS strongly preferred, as we are standardizing on AWS as we move off Azure Databricks
- Demonstrated ability to independently prototype, benchmark, and evaluate new technologies, translating results into clear, pragmatic adoption decisions
- Comfortable with Git, code reviews,



and writing tested, maintainable, well‑documented code
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience
- Professional fluency in written and spoken English: Strong communication skills to effectively collaborate with distributed engineering, product, and business teams across North America, India, and Europe

What You’ll Do

- Team Leadership & Mentorship: Lead and mentor data engineers on the Madrid team, providing clear technical direction and constructive code reviews, while partnering closely with distributed engineers across North America and India
- Hands‑On Delivery & Pipeline Ownership: Design, build, and optimize robust ETL/ELT pipelines that move and transform millions of daily data points reliably and efficiently — staying hands‑on in the code rather than delegating it away
- AI‑Assisted Development: Champion the use of AI coding assistants and prompt‑based development to accelerate prototyping, code generation, refactoring, testing, and documentation, and establish best practices and review standards for your team
- Hands‑On POCs & Prototyping: Personally build proofs‑of‑concept to validate new tools and patterns (e.g., ClickHouse, dbt, open‑source orchestration frameworks) before broader rollout, and translate results into clear recommendations
- Platform Modernization: Contribute to the hands‑on migration away from Azure Databricks toward a more open, flexible, AWS‑based stack, minimizing disruption to high‑volume daily data delivery
- Deep System Integration: Understand the systems upstream and downstream of your pipelines, from ingestion through to c

📌 Data Engineering Manager (Madrid)
🏢 MediaRadar
📍 Madrid