Data Science Analyst

About the role

Metyis is seeking a Data Science Analyst with at least two years of experience to join its international Data & Analytics team in Madrid. The role focuses on end‑to‑end data handling, from acquisition to visualization, within a collaborative, fast‑growing environment.

Key responsibilities

• Design, build, and maintain data pipelines in Palantir Foundry using Python and PySpark, covering source ingestion to consumption datasets.
• Implement and evolve the Foundry data model and ontology to support customer use cases.
• Translate functional business requirements into technical solutions, estimate effort, and document design decisions.
• Ensure data quality through validations, health checks, unit tests, schema control, and monitoring.
• Optimize transformation performance and resource consumption, including partitioning and incremental processing.
• Develop consumption layers and lightweight analytic applications on Foundry (Workshop, Contour, Quiver) when needed.
• Apply software‑engineering best practices such as version control, code review, branch strategy, cross‑environment promotion, and deployment automation.
• Collaborate with client data and IT teams to integrate new data sources and commission solutions.

Required profile

• Minimum 2 years of professional experience in data science or related analytics roles.
• Proven ability to work with large‑scale data pipelines and ensure data quality.
• Strong problem‑solving skills and ability to translate business needs into technical designs.

Required skills

• Palantir Foundry (Pipeline Builder, code repositories)
• Python programming
• PySpark / Apache Spark
• Git version control
• Data modeling and ontology design

What we offer

• Regular interaction with senior client stakeholders to drive impactful change.
• Opportunity to become the go‑to person for end‑to‑end data handling and analytics.
• Leadership of a team creating data management pipelines and visualization products.
• Collaboration with data scientists across the data lifecycle.
• Membership in a fast‑growing, international, and diverse team.