Senior/ Lead Data Engineer with industrial knowledge (Freelancer)

The role is a freelance Senior / Lead Data Engineer position focused on industrial data and AI-driven analytics within the chemical and process industry. The cooperation model is flexible, project-based, and KPI-driven, involving short- to mid-term contracts typically lasting 3-8 weeks. The engineer will work with real industrial and production data supporting digitalization initiatives.

• 5+ years of experience in Data Engineering, industrial analytics, or data solution delivery
• Strong Python and SQL skills for building ingestion pipelines, transformations, and validation logic
• Proven experience in building reproducible, auditable, and scalable data products
• Hands-on experience with industrial and operational data including MES, SCADA, Historians, PLC/OT systems, and operational time-series data
• Solid background in data profiling and data quality assessment including anomaly detection, gap analysis, dead signal analysis, and inconsistency checks
• Ability to design datasets aligned with business KPIs and PoC objectives
• Strong engineering discipline including Git-based workflows, code reviews, testing practices, documentation, and runbooks
• Experience working in PoC-driven, KPI-oriented project environments
• English level B2 or higher

Nice to have:

• Experience with Databricks, Apache Spark, Snowflake, or lakehouse platforms
• Familiarity with cloud environments (AWS and/or Azure)
• Experience building PoC tooling or visualizations using Streamlit, Plotly, or Power BI
• Understanding of industrial / OT environments and historian-based data models
• Exposure to analytics or ML use cases in manufacturing, process industry, energy, chemical, or pharma sectors
• Experience using AI tools in day-to-day engineering workflows

• Design and implement ingestion and transformation pipelines from industrial source systems into clean, auditable datasets
• Work directly with data from MES, SCADA, historians, PLC/OT systems, LIMS/LAB/OPS platforms, and other operational sources
• Perform data quality audits and identify anomalies, dead or inactive signals, data gaps, and inconsistencies
• Develop datasets and validation logic supporting KPI definitions and PoC delivery
• Build PoC components such as batch analytics pipelines, event detection logic, and time-series transformations
• Create lightweight PoC tooling, dashboards, applications, or visualizations when required
• Support presales and consulting teams by shaping technical solutions and identifying business value hidden in industrial data
• Produce delivery-grade documentation, handover materials, and implementation support assets