Senior/ Lead Data Engineer with industrial knowledge (Freelancer)

Spyrosoft is a software engineering company established in 2016, specializing in technology solutions for industry 4.0, automotive, geospatial, healthcare & life sciences, employee experience & education, and financial services industries. The company was recognized as one of the fastest growing technology companies in Europe in 2021 and 2022 by the Financial Times.

• 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, and operational time-series data
• Solid background in data profiling and data quality assessment including anomaly detection, gap analysis, dead signal analysis, and inconsistency checks
• Ability to design datasets aligned with business KPIs and PoC objectives
• Strong engineering discipline including Git-based workflows, code reviews, testing practices, documentation, and runbooks
• Experience working in PoC-driven, KPI-oriented project environments
• English language proficiency at B2 level or higher
• Experience 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 such as 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

• 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, and inconsistencies
• Develop datasets and validation logic supporting KPI definitions and PoC delivery
• Build PoC components such as batch analytics pipelines, event detection logic, and 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