Are you excited about building modern data infrastructure that empowers self-service analytics at scale? At Super Technologies, data is a strategic enabler of decision-making, experimentation, and performance measurement. Our Analytics Platform team builds the foundations that make data reliable, accessible, and actionable-partnering with product and business teams to measure what matters.
You'll be joining a mature and growing community of 40+ data engineers who are shaping one of the most advanced data ecosystems in the industry.

 

What you'll be doing:

• design, build, and optimize scalable ETL/ELT pipelines for high-volume, fast-changing datasets
• apply proven data warehouse methodologies (e.g. Kimball, Inmon) to design and deliver trustworthy, performant data models
• develop and maintain semantic layers and curated data marts to enable analytical self-service across the company
• orchestrate reliable workflows with modern tooling such as Apache Airflow
• enforce data quality, lineage, and observability practices to ensure confidence in our data products
• partner with product, analytics, and engineering teams to co-create scalable data solutions that deliver measurable business value
• raise the engineering bar through code reviews, testing, automation, and architectural improvements

We're looking for:

• strong SQL and Python skills, with solid understanding of distributed data systems and modern data architectures
• deep knowledge of dimensional data modeling (Kimball, Inmon) and hands-on experience building analytical layers in DWHs such as Snowflake or BigQuery
• hands-on experience with cloud platforms (AWS preferred, Azure/GCP also valuable)
• workflow orchestration experience with Airflow or similar tools
• strong ownership mindset-able to work independently, structure work, and partner effectively with technical and non-technical stakeholders
• excellent communication skills with a collaborative, problem-solving approach
• based in Madrid or surrounding area as this will require hybrid work

Bonus points for:

• experience with real-time streaming technologies (Kafka or similar)
• experience with cloud native Analytical databases like Snowflake
• multi-cloud exposure and understanding of platform trade-offs
• background in product-led or experimentation-driven environments
• experience with data governance, cataloging, and documentation practices (e.g. DataHub)