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.

About the role:

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.

Why Is It Worth Joining Us?

🔥 Why join our Chemical Industry Software Engineering team?

➡️ You will work on real industrial and manufacturing data challenges within the chemical industry

➡️ You will help shape PoC initiatives that directly influence measurable business outcomes

➡️ You’ll gain exposure to industrial analytics, operational time-series data, and AI-driven use cases

➡️ You’ll collaborate with engineers and consultants experienced in OT systems, industrial operations, and scalable data platforms

➡️ You will have direct influence on technical approaches, data validation strategies, and solution design during early project phase

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

• Experienced in using AI tools in day-to-day engineering workflows

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

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