About the Role
Imagine this. Every engine and RUTA test cell, every calibration station, and every production line in Hengelo generates data, and that data holds the answers our teams need to build faster, catch failures earlier, and ship with confidence. As Data Engineer, you will be the person who turns that raw, scattered data into reliable pipelines, clean datasets, and tools the whole production organization can trust and use.
This is a hands-on role close to the shop floor and the test cells. You will work directly with test logs, DAQ and ECU data, quality records, and shipment data, building the infrastructure and dashboards that make production decisions faster and better.
At Destinus, we are revolutionizing the defense industry with cutting-edge Unmanned Aerial Vehicles (UAVs). Our innovative technologies are designed to meet the unique demands of modern defense operations, delivering unparalleled speed, precision, and cost effectiveness. Destinus partners with government agencies and defense organizations worldwide to provide advanced solutions for mission-critical operations, enabling a new era of efficiency and technological superiority. Join us in shaping the future of defense with groundbreaking aerospace innovations.
What You'll Do
- Design, build, and maintain data pipelines that collect and consolidate test, calibration, quality, and shipment data from production and test operations
- Extract and structure data from test logs, DAQ systems, and ECU outputs into clean, analysis-ready datasets
- Build and maintain dashboards and reporting tools that give Production, Test Operations, and Engineering visibility into yield, failure trends, and product performance
- Support root cause analysis and supplier comparison work by preparing and validating datasets used across engineering and quality investigations
- Ensure data quality, consistency, and traceability across systems, flagging gaps or anomalies before they propagate into analysis or reporting
- Work with Test Operations, RCA, and Quality teams to understand their data needs and translate them into scalable data solutions
- Automate recurring data extraction, cleaning, and reporting tasks currently done manually, using Python and scripting tools
- Maintain and evolve the data infrastructure supporting engine lifecycle tracking, batch consistency analysis, and failure reporting
- Document data sources, definitions, and pipeline logic so datasets remain understandable and usable across teams
- Collaborate closely with Production, Test Operations, Data & Validation Engineering, and Industrialization to keep production data flowing and actionable
Requirements
What You'll Need
- Bachelor's or Master's degree in Data Engineering, Computer Science, Electrical Engineering, Industrial Engineering, or a related technical field
- Strong experience building and maintaining data pipelines, ideally within a manufacturing, test, aerospace, or other hardware-heavy environment
- Proficiency in Python and SQL, with hands-on experience using pandas or similar tools for data cleaning, transformation, and analysis
- Experience building dashboards or reporting tools (e.g. HTML, Power BI, or equivalent) that communicate technical data clearly to non-technical stakeholders
- Understanding of test and measurement data (e.g. DAQ logs, sensor data, calibration records) is a strong plus
- Experience working with messy, real-world data from multiple systems and reconciling it into a single reliable source of truth
- Ability to work closely with engineering and production teams to understand what data they actually need, not just what is easiest to extract
- Strong attention to detail and a rigorous approach to data validation and traceability
- Professional working proficiency in English. Dutch is a plus.
Who You Are
You are a hands-on data professional who enjoys getting close to messy, real-world production data and turning it into something people can actually use and trust. You take ownership of your pipelines end to end, and you thrive working alongside engineers and production teams rather than at a distance from them.
