About Gapstars
At Gapstars, we partner with some of Europe’s most ambitious tech companies, from disruptive startups to fast-scaling scaleups, helping them build high-performing remote engineering teams. Headquartered in the Netherlands, with talent hubs in Sri Lanka and Portugal, we are home to 275+ engineers who thrive on solving real-world challenges with modern technologies. Our teams work across domains, from networking and marketplaces to SaaS and AI, delivering scalable solutions that drive meaningful outcomes. If you’re looking for a company that combines technical excellence, a strong culture, and room to grow, welcome to Gapstars.
About the Role
We are looking for a Lead Data Engineer / Analytics professional to join our client in a hybrid role that sits between the Data Engineering and Data Science teams. This position is intended to directly support the Data Science team by building internal tools, automating recurring workflows, and handling data engineering tasks that are not always prioritized by the core Data Engineering function.
The primary goal of this role is to make the Data Science team more efficient by improving how they access, transform, enrich, and utilize data. This is not a pure Data Science role. Instead, it is a strongly data engineering-focused position for someone who understands analytics well enough to work closely with data scientists and solve practical data problems in their environment.
This person will report into the relevant engineering structure while being dedicated to supporting the needs of the science team on a day-to-day basis.
Data Engineering & Automation
Build, maintain, and improve internal data pipelines that support the needs of the Data Science team.
Automate recurring manual workflows and operational processes using Python.
Transform, clean, and enrich data from a variety of internal and external sources.
Develop reusable scripts, tools, and lightweight frameworks that improve team productivity.
Create simplified data views, models, and libraries that enable efficient and reliable data access for analytics and science use cases.
Analytics Enablement
Work closely with data scientists to understand their data needs and translate them into scalable engineering solutions.
Support the preparation and structuring of datasets for experimentation, analysis, and model-related workflows.
Improve the availability and usability of analytical data assets.
Help bridge the gap between raw data engineering work and the practical analytical needs of the science team.
Cross-functional Collaboration
Partner with stakeholders across the Data Engineering and Data Science teams.
Operate with a high level of ownership and independence in a distributed team environment.
Communicate clearly on progress, blockers, and solutions, especially given cross-time-zone collaboration.
Contribute to internal documentation, process improvements, and best practices.
6+ years of experience in data science and/or data engineering
Strong hands-on experience with Python and solid data engineering fundamentals.
Proven experience building, maintaining, and optimizing data pipelines.
Experience working with a columnar data warehouse, such as Snowflake, Redshift, or BigQuery.
Strong understanding of data transformation, data modeling, and efficient data handling practices.
Ability to work independently and proactively in a remote or distributed team setup.
Strong attention to detail and a structured way of working.
Good communication skills and the ability to collaborate effectively across technical teams.
Preferred
Experience with Snowflake is a plus, but not a strict requirement.
Exposure to analytics-focused environments or close collaboration with Data Science teams.
Familiarity with building simplified data layers, reusable libraries, or internal tooling for analytics users.
Understanding of analytics workflows and the challenges faced by Data Science teams.
