Data Engineer

We're building the data backbone for large-scale SCADA deployments, moving telemetry from field devices, RTUs, PLCs, and historians into reliable, queryable platforms that operations, maintenance, and engineering teams depend on daily. This is not a generic ETL role. You'll work where operational technology meets modern data infrastructure: high-frequency time-series at scale, protocol-level integration, strict network segmentation, and data that people use to make real-time decisions about physical assets. If you've dealt with tag mapping, out-of-order sensor data, or the reality that a historian's timestamps and your ingestion clock rarely agree, you'll be at home here.

Responsibilities:

• Design, build, and operate ingestion pipelines from SCADA sources, historians, RTUs, PLCs, IEDs, and edge gateways into our central data platform.
• Work with industrial protocols and interfaces (OPC UA/DA, Modbus, DNP3, IEC 60870-5-104, IEC 61850, MQTT/Sparkplug B) and the connectors that sit on top of them.
• Build streaming and batch pipelines for high-volume time-series data, handling late arrivals, deadband compression, gaps, backfills, and clock drift.
• Model asset hierarchies and tag namespaces so that raw point IDs become something an engineer can actually query.
• Own data quality: validation, reconciliation against source historians, alerting on stale or silent tags, and clear lineage from field device to dashboard.
• Partner with OT and network teams to move data across segmented environments (Purdue-model zones, DMZs, unidirectional gateways) without compromising security posture.
• Support downstream consumers with condition monitoring, predictive maintenance, energy analytics, KPI, and regulatory reporting.
• Instrument and monitor your own pipelines; participate in an on-call rotation for critical data flows [adjust or remove].

Requirements:

• 3+ years building production data pipelines.
• Strong Python and SQL.
• Hands-on experience with a distributed streaming or processing framework (Kafka, Flink, Spark Structured Streaming, or equivalent).
• Time-series data at scale: You've worked with a purpose-built store (TimescaleDB, InfluxDB, ClickHouse, or a historian) and understand why a general-purpose RDBMS struggles here.
• Workflow orchestration (Airflow, Dagster, Prefect, or similar).
• Comfort with Linux, containers, Git, and CI/CD.
• Practical grasp of data modelling, schema evolution, and idempotent pipeline design.
• Ability to work with engineers who speak in tag names and equipment IDs rather than tables and columns.

Strongly preferred:

• Direct experience with SCADA or industrial historian platforms AVEVA/OSIsoft PI, Wonderware, GE Proficy/iFIX, Siemens WinCC, Schneider EcoStruxure/ClearSCADA, and Ignition.