Senior Data Engineer - Industrial Data Platforms

This role sits at the intersection of Operational Technology and Enterprise IT, focusing on building robust, AI-ready data foundations. You will design and implement scalable architectures that power Smart Factory, Digital Twin, and advanced analytics initiatives. The position requires a hands-on engineer capable of bridging the gap between raw industrial signals and actionable business intelligence.

Our client is a leading global professional services firm specializing in audit, tax, and advisory services. They are currently expanding their Industrial Digital & AI practice to help global manufacturing clients navigate complex digital transformations. The firm is known for its collaborative culture, commitment to innovation, and providing high-impact solutions to diverse industrial sectors.

Responsibilities

• Design and maintain scalable batch and streaming pipelines for diverse industrial data sources including Historian, IoT, PLC/SCADA, MES, and ERP systems.
• Contextualize raw operational signals against production orders, assets, and shifts to create high-quality, analysis-ready datasets.
• Implement rigorous data quality frameworks, lineage, metadata management, and observability controls to ensure data reliability.
• Develop secure APIs and data models to support dashboards, optimization engines, and machine learning workloads.
• Collaborate within cross-functional teams to deliver production-grade solutions while ensuring effective documentation and system performance.
• Apply industrial connectivity patterns and time-series data management to solve complex integration challenges.

Requirements

• 4-7 years of professional experience in data engineering, analytics engineering, or industrial data integration.
• Proficiency in SQL and Python with a proven track record of building production-grade ETL/ELT pipelines.
• Hands-on experience with cloud data platforms, data lakehouses, and modern orchestration frameworks.
• Strong understanding of industrial connectivity patterns, time-series data, and message brokers.
• Experience with Databricks, Microsoft Fabric, Azure Data Factory, Spark, or Kafka.
• Knowledge of ISA-95 standards, OPC UA, MQTT, and digital twin data modeling.
• Exposure to MLOps, containerization, and CI/CD practices.
• Bachelor’s degree in Engineering, Computer Science, Information Technology, or a related technical discipline.