About the job
About Gapstars
At Gapstars, we collaborate with some of Europe’s most innovative tech companies, ranging from disruptive startups to rapidly scaling enterprises. Our mission is to help them establish high-performing remote engineering teams. With our headquarters in the Netherlands and talent hubs in Sri Lanka and Portugal, we are proud to host over 275 engineers dedicated to tackling real-world challenges using cutting-edge technologies. Our teams operate across a variety of domains, including networking, marketplaces, SaaS, and AI, delivering scalable solutions that yield impactful results. If you seek a workplace that values technical proficiency, fosters a robust culture, and encourages professional growth, you’ll find your home at Gapstars.
About the Role
We are in search of a Lead Data Engineer / Analytics professional to join our team in a hybrid capacity that bridges the Data Engineering and Data Science teams. This role is designed to directly assist the Data Science team by creating internal tools, automating repetitive workflows, and managing data engineering tasks that may not always be prioritized by the primary Data Engineering function.
Your main objective will be to enhance the efficiency of the Data Science team by optimizing their access to, transformation of, enrichment of, and utilization of data. This position emphasizes data engineering rather than pure Data Science, and is ideal for someone with a strong understanding of analytics who can closely collaborate with data scientists to resolve practical data challenges.
This individual will report to the relevant engineering structure while being primarily dedicated to supporting the daily needs of the science team.
Data Engineering & Automation
Construct, maintain, and enhance internal data pipelines that meet the needs of the Data Science team.
Automate recurring manual tasks and operational processes using Python.
Transform, clean, and enrich data sourced from various internal and external origins.
Develop reusable scripts, tools, and lightweight frameworks to boost team productivity.
Create simplified data views, models, and libraries that facilitate efficient and reliable data access for analytics and scientific applications.
Analytics Enablement
Collaborate closely with data scientists to understand their data requirements and translate them into scalable engineering solutions.
Assist in the preparation and structuring of datasets for experimentation, analysis, and model-related workflows.
Enhance the availability and usability of analytical data assets.
Help bridge the gap between fundamental 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.
Function within a high-performance culture that prioritizes collaboration and innovation.
