Data Engineer - IoT Intelligence + ETL Pipelines

KEY RESPONSIBILITIES

• Build the IoT sensor uncertainty registry, Data Quality Score computations, and cross-sensor + lab validation API/endpoints.
• Own GraphDB endpoints for IoT metadata — ingestion, integration, versioning, and materialization of virtual sensors in ClickHouse.
• Drive data-quality governance across the platform's sensor and plant datasets.
• Build legacy/offline data ingestion pipelines into MongoDB.
• Design and maintain ETL pipelines that support IoT-based ML workflows.

SKILLS - MUST HAVE

• Strong SQL + Python for production data pipelines
• Time-series/IoT data at scale — irregular sampling, gaps, sensor drift, resampling, deduplication.
• A columnar/analytical store, ideally ClickHouse (materialized views, projections, query optimisation).
• Pipeline/ETL engineering — reliable, monitored batch + streaming ingestion with idempotency and backfill.
• API construction — clean, documented data endpoints.
• Data-quality/validation mindset — range/threshold logic, cross-source reconciliation, measurement uncertainty.
• Cloud data infra on AWS (EC2/S3 minimum) and a Git/CI-based workflow

SKILLS - NICE TO HAVE

• Domain adjacency — water/wastewater, process/industrial, SCADA, PLC/Modbus.
• Light MLOps experience — MLflow, SageMaker, model versioning.
• MQTT / Node-RED / edge telemetry exposure.
• Statistical fluency — uncertainty quantification, error bounds
• Graph databases (Neo4j / Cypher) — big plus.