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