Data Scientist – Data Migration (Python, Databricks)

You will join a product team focused on marketing effectiveness, contributing to a key data platform modernization initiative.

The work combines data pipelines, business logic, and validation. Existing workflows written in R and SQL will be translated into a modern Python-based environment, with a strong focus on data accuracy, consistency, and clarity of meaning.

This is a Data Scientist role with a strong focus on data pipelines, validation, and business logic.

What you’ll work on

• Drive the modernization of data pipelines, translating R + SQL workflows into Python (PySpark, pandas)
• Transition data processing from legacy platforms (Impala) to Databricks
• Review and refine business logic embedded in existing pipelines
• Validate outputs by comparing new datasets with existing ones and aligning results
• Collaborate with data scientists and stakeholders to ensure consistency and reliability of data
• Document transformations, logic, and decisions to support long-term maintainability

Core requirements

• Python programming, particularly in data science and analytics.
• Experience working with Databricks for data engineering and analytics tasks.
• Strong problem-solving skills and ability to work independently and collaboratively.
• Advanced SQL knowledge
• Python libraries: PySpark, pandas
• R: Ability to read & understand existing R (tidyverse) script. Experience with RStudio would be beneficial.
• Technologies: Databricks, GitHub

Nice to have

• Experience with marketing data (Google, Meta, etc.)
• Understanding of data-driven marketing and effectiveness analysis

Why join

• Work on data that directly supports decisions in a globally recognized consumer product environment
• Take ownership of pipelines and the business logic behind them, with real influence on how data is defined and trusted
• Contribute to a modern data platform (Databricks, Python) and shape how existing systems evolve
• Collaborate with experienced data scientists and stakeholders across teams, with space to challenge assumptions and improve outcomes
• Join a product-focused setup where engineers and data scientists act as full team members, contributing beyond implementation