We are looking for a Senior / Lead Data Engineer (Freelance) to join project-based initiatives focused on industrial data and AI-driven analytics within the chemical and process industry. Engagement model: ✅Freelance cooperation ✅Part-time or full-time involvement ✅Sequential project-based work ✅Hourly salary: 140 - 200 PLN The cooperation model is flexible and based on short- to mid-term contracts, typically connected to KPI-driven Proof of Concepts (PoCs) and industrial analytics initiatives. Projects usually last 3-8 weeks, with new opportunities appearing every 1-2 months. You will work with real industrial and production data, supporting digitalization initiatives that directly impact measurable business outcomes. The role combines hands-on data engineering with early-stage solution design and close cooperation with consulting and presales teams. Tech stack: Python SQL Databricks / Apache Spark Snowflake / Lakehouse architectures AWS or Azure Streamlit, Plotly, Power BI Industrial data sources: MES, SCADA, Historians, PLC/OT, LIMS, ELN Requirements: 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, Operational time-series data Solid background in data profiling and data quality assessment, including: Anomaly detection Gap analysis Dead signal analysis Inconsistency checks Ability to design datasets aligned with business KPIs and PoC objectives Strong engineering discipline: 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 Experienced in using AI tools in day-to-day engineering workflows Main responsibilities: 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 Inconsistencies Develop datasets and validation logic supporting KPI definitions and PoC delivery Build PoC components such as: Batch analytics pipelines Event detection logic 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