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 flexible, 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, versátil, 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 client‑facing platfo

📌 Data Engineering Manager (Madrid)
🏢 MediaRadar
📍 Madrid