Senior/ Lead Data engineer with industrial knowledge (Freelancer)

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