Senior/ Lead Data Engineer with industrial knowledge (Freelancer)

Engagement model:

✅Freelance cooperation

✅Part-time or full-time involvement

✅Sequential project-based work

✅Hourly salary: 140 - 200 PLN

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