Data Engineer
Data Engineer building and optimizing ETL/ELT pipelines on an open-source, AWS-based stack (dbt, ClickHouse, Airflow/Dagster, Python, SQL) for an ad intelligence platform, migrating away from Azure Databricks.
- Compensation
- Not disclosed
- Location
- Madrid, Community of Madrid, Spain
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 7+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 4-7+ years in data engineering / ETL with hands-on production pipeline experience
- Strong SQL and RDBMS skills: schema design, performance tuning, complex query optimization
- Hands-on experience with dbt, ClickHouse, and open-source orchestration tools (Airflow, Dagster)
- Strong proficiency in Python (or similar) for building and automating data pipelines
- Hands-on experience using AI coding assistants (GitHub Copilot, Claude, ChatGPT) with verification and testing
- Hands-on experience with a major cloud platform; AWS strongly preferred
- Comfortable with Git, code reviews, and writing tested, maintainable, well-documented code
- Bachelor's or Master's degree in Computer Science, Engineering, or related field, or equivalent practical experience
Nice to have
- Experience with cloud data warehouses or lakehouse patterns (Snowflake, BigQuery, Redshift)
- Experience with streaming / real-time data technologies such as Kafka
- Familiarity with containerization (Docker, Kubernetes) and infrastructure-as-code (Terraform)
- Previous experience in Advertising, Media, or Market Research
Full description
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 Engineer who can join our Madrid-based team and help build the next generation of our data delivery platform, working closely with senior and principal engineers and engineering leadership across North America and India to design, build, and operate the pipelines that move and transform millions of data points every day.
We are deliberately moving away from a locked-in, vendor-heavy stack 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 new tools before we adopt them at scale.
Requirements
What You've Done:
- 4-7+ years in data engineering / ETL, with a strong track record as a hands-on engineer building and operating production data pipelines
- Strong SQL and RDBMS skills with solid, hands-on experience schema design, performance tuning, 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, Claude, ChatGPT), with the judgment to verify, test, and refine AI-generated code and queries
- Hands-on experience with a major cloud platform; AWS strongly preferred, as we are standardizing on AWS as we move off Azure Databricks
- 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
What You'll Do:
- Design, build, and optimize robust ETL/ELT pipelines that move and transform millions of daily data points reliably and efficiently
- Build and maintain production data workflows using tools such as dbt, ClickHouse, and open-source orchestration frameworks (Airflow, Dagster, or similar)