Data Engineer

We are building the data backbone for large-scale SCADA deployments, moving telemetry from field devices, RTUs, PLCs and industrial historians into reliable, queryable platforms used by operations, maintenance and engineering teams. This is not a generic ETL role. You will work at the intersection of Operational Technology (OT) and modern data infrastructure, handling high-frequency time-series data, industrial protocols, strict network segmentation and data used for real-time decisions on physical assets. If you have worked with SCADA tags, historian data, out-of-order sensor events, timestamp mismatches, data gaps or tag mapping, this role will be a strong fit.

We are looking for a hands-on Data Engineer with strong production pipeline experience who can work with both modern data engineering infrastructure and industrial/SCADA data. The ideal candidate understands that industrial data has different challenges from conventional application data, including high-frequency telemetry, time-series storage, late/out-of-order events, tag hierarchies, historian reconciliation and OT network constraints. Candidates with experience in SCADA, industrial IoT, historians, railway/metro systems or other OT environments will be strongly preferred.

Responsibilities:

• Design, build and operate ingestion pipelines from SCADA historians, RTUs, PLCs, IEDs and edge gateways into the central data platform.
• Work with industrial protocols and interfaces such as OPC UA/DA, Modbus, DNP3, IEC 60870-5-104, IEC 61850, MQTT and Sparkplug B.
• Build streaming and batch data pipelines for high-volume time-series data.
• Handle late-arriving data, deadband compression, missing data, backfills and clock drift.
• Model asset hierarchies and tag namespaces so raw point IDs become usable engineering data.
• Own data quality through validation, source reconciliation, stale/silent tag detection, alerting and lineage.
• Partner with OT and network teams to move data across Purdue-model zones, DMZs and unidirectional gateways without compromising security.
• Support downstream use cases including condition monitoring, predictive maintenance, energy analytics, KPI reporting and regulatory reporting.
• Instrument, monitor and troubleshoot production data pipelines.
• Participate in an on-call rotation for critical data flows, where required.

Requirements:

• 3+ years of experience building production data pipelines.
• Strong Python and SQL skills.
• Hands-on experience with distributed streaming/processing frameworks such as Kafka, Apache Flink, Spark Structured Streaming or equivalent.
• Experience handling large-scale time-series data.
• Hands-on experience with TimescaleDB, InfluxDB, ClickHouse, industrial historians or equivalent time-series platforms.
• Understanding of why high-volume time-series workloads require purpose-built data stores rather than only general-purpose RDBMS solutions.
• Experience with workflow orchestration tools such as Airflow, Dagster, Prefect or similar.
• Comfortable working with Linux, containers, Git and CI/CD.
• Strong understanding of data modelling, schema evolution and idempotent pipeline design.
• Ability to work closely with OT/industrial engineers and understand tag names, equipment IDs and asset hierarchies.

Good to Have:

• Direct experience with SCADA or industrial historian platforms, including:
• AVEVA / OSIsoft PI
• Wonderware
• GE Proficy / iFIX
• Siemens WinCC
• Schneider EcoStruxure / ClearSCADA
• Ignition
• Railway / Metro domain experience, particularly:
• Traction power SCADA
• Tunnel ventilation
• ECS/BMS
• Signalling and interlocking
• ATS
• AFC
• Rolling stock condition monitoring
• Depot systems
• Passenger information systems
• Knowledge of OT security practices, including IEC 62443, network segmentation and read-only data extraction patterns.
• Experience with cloud/IoT platforms such as Azure IoT Hub, AWS IoT or GCP.
• Experience with edge-to-cloud data architectures.
• Exposure to rail assurance standards EN 50126 / EN 50128 / EN 50129.
• Experience working in regulated engineering environments.
• Exposure to dbt, Kubernetes and Terraform.