Data Engineer
AI Summary
Designs, implements and operates an industrial data lakehouse and end-to-end data pipelines for IIoT telemetry, machine connectivity and advanced analytics.
About this role
Overview
Data Engineer / DataOps / IoT Expert
8–9 YEARS · 1 FTE · BANGALORE
▪Designs, implements and operates the industrial data lakehouse — Spark, Iceberg, MinIO, InfluxDB, PostgreSQL
▪Builds end-to-end data pipelines: ingestion, quality, validation and governance
▪Implements and maintains data gateways for machine and sensor connectivity
▪Deploys advanced AI components — Qdrant, Neo4j, BrainCube analytics integration
▪Delivers clean, reliable data flows for advanced analytics; optimizes data infrastructure with DevOps and application teams
Responsibilities
- Designs and runs the lakehouse + end-to-end data pipelines.
- Owns ingestion, data quality, validation and governance.
- Stands up and maintains data gateways for machines and sensors.
- Feeds clean, reliable data to the analytics and dashboard layers.
- Works with DevOps to optimize the data infrastructure.
Qualifications
IIoT, time-series / telemetry data, manufacturing analytics, OEE.
Essential skills
- Must-have: lakehouse + streaming, ideally time-series (InfluxDB / IIoTtelemetry) — not just batch BI.
- The Iceberg + Spark + MinIO combo is specific; open-table-formatexperience really stands out.
- Ask how they'd tame a chatty sensor firehose plus late-arriving data.
Desired skills
- Neo4j / Qdrant is nice-to-have (it feeds the AI/analytics layer), not a gate.
Skills
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