Data Engineer

MediaRadar equips marketing, sales, and analytics leaders with 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 over 35 million ad and campaign assets and $280 billion in media spend, MediaRadar provides a single, interoperable source of truth that integrates seamlessly with enterprise analytics and AI systems, delivering faster, cleaner, and more actionable intelligence.

The Role

We are seeking a Data Engineer to join our Madrid-based team and contribute to the next generation of our data delivery platform. You will collaborate with senior and principal engineers across North America and India to design, build, and operate pipelines that handle millions of data points daily.

The role involves transitioning from a vendor-heavy stack to a flexible, open-source architecture. Your responsibilities will include writing code, designing schemas, tuning queries, and evaluating new tools before scaling them. This is a hands-on builder position focused on production pipelines, database tuning, and adopting open-source tools that shape our platform.

Requirements

What You've Done:

• 4-7+ years in data engineering or ETL, with a proven track record as a hands-on engineer building and operating production data pipelines
• Strong SQL and RDBMS skills, including schema design, performance tuning, and complex query optimization
• Experience with tools like dbt, ClickHouse, and open-source pipeline/orchestration tools (e.g., Airflow, Dagster), with the ability to select the right tool for the task
• Proficiency in Python (or a similar language) for building and automating data pipelines
• Experience with AI coding assistants (e.g., GitHub Copilot, Claude, ChatGPT) and effective prompting techniques, including verifying, testing, and refining AI-generated code and queries
• Hands-on experience with a major cloud platform; AWS is strongly preferred as we standardize on it, moving away from Azure Databricks
• Familiarity 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 to move and transform millions of daily data points reliably and efficiently
• Develop and maintain production data workflows using tools like dbt, ClickHouse, and orchestration frameworks (Airflow, Dagster)
• Write and optimize complex SQL, including schema design, indexing, performance tuning, and query optimization
• Contribute to migrating from Azure Databricks to a more open, flexible, AWS-based stack while minimizing disruption to high-volume daily data delivery
• Build proofs-of-concept to benchmark new tools and patterns, providing clear results for adoption decisions
• Leverage AI coding assistants and well-crafted prompts to accelerate pipeline development, SQL generation, debugging, testing, and documentation—always reviewing and validating outputs before deployment
• Implement testing, validation, monitoring, observability, and CI/CD practices to ensure data accuracy and pipeline health at scale
• Understand upstream and downstream systems of your pipelines, from ingestion to client-facing platforms, ensuring clean, end-to-end data delivery
• Collaborate with engineers in North America and India, participate in code reviews, and learn the nuances of the Advertising and Market Research domain

Bonus Points (Nice to Haves):

• Experience with cloud data warehouses or lakehouse patterns (e.g., Snowflake, BigQuery, Redshift)
• Experience with streaming or real-time data technologies like Kafka
• Familiarity with containerization (Docker, Kubernetes) and infrastructure-as-code (e.g., Terraform)
• Previous experience in Advertising, Media, or Market Research

This role is remote-based in Madrid, with the expectation of periodic in-person collaboration at a local WeWork space.