Senior Data Engineer, Industrial IoT

Senior Data Engineer, Industrial IoT

Join to apply for the Senior Data Engineer, Industrial IoT role at Qcells North America.

This range is provided by Qcells North America. Your actual pay will be based on your skills and experience—talk with your recruiter to learn more.

$/yr - $/yr

We are looking for a Senior Data Engineer specializing in Industrial IoT to architect and build the data foundation for our next‑generation data center energy management platform. The ideal candidate will have deep expertise in designing data pipelines for time‑series IoT data, building semantic data models, and creating scalable data integration frameworks that transform raw industrial telemetry into analytics‑ready datasets. You will own the entire data lifecycle from ingestion to semantic enrichment, working at the intersection of operational technology and modern data platforms.

This position is remote and offers the opportunity to define how critical infrastructure data is collected, modeled, and consumed across AI/ML applications.

• Design and implement end‑to‑end data pipelines ingesting millions of data points per minute from industrial IoT devices.
• Build semantic data models and metadata management systems that add business context to raw telemetry streams.
• Create data transformation frameworks that normalize heterogeneous device data into unified schemas.
• Develop automated data quality validation, anomaly detection, and data lineage tracking systems.
• Design and maintain a centralized device metadata repository and configuration management database.
• Implement streaming ETL pipelines using Apache Kafka, Spark Streaming, or cloud‑native services.
• Build time‑series data storage solutions optimized for both real‑time analytics and historical analysis.
• Create data APIs and access patterns supporting both operational dashboards and ML model training.
• Partner with ML engineers to ensure data pipelines meet feature engineering requirements.
• Establish data governance practices including schema versioning, backwards compatibility, and change management.
• Collaborate with field engineers to understand device characteristics and improve data collection strategies.

• Bachelor's degree in Computer Science, Data Science, Information Systems, or related field.
• 10+ years in data engineering with 5+ years focused on IoT, time‑series, or streaming data platforms.
• Expert‑level experience with streaming data technologies (Kafka, Pulsar, Kinesis, Event Hubs).
• Strong expertise in time‑series databases (Influx
  
  DB, Timescale
  
  DB, Azure Data Explorer, AWS Timestream).
• Proficiency in Python or Scala for data pipeline development and SQL for complex data transformations.
• Experience with schema management, data catalogs, and metadata‑driven architectures.
• Deep understanding of data modeling for analytical workloads including dimensional modeling and data vault.
• Hands‑on experience with cloud data platforms (Azure Synapse, AWS Glue, Databricks, Snowflake).
• Knowledge of data formats and serialization (Parquet, Avro, Protocol Buffers, JSON Schema).
• Experience building real‑time data quality monitoring and alerting systems.
• Travel may be required up to 10% for architecture reviews and stakeholder alignment.

• Solar Industry experience (Renewable).
• Experience with industrial protocols (OPC‑UA, Modbus, MQTT) and IoT data standards.
• Knowledge of semantic web technologies (RDF, OWL, knowledge graphs, ontologies).
• Familiarity with Open Telemetry, Prometheus, or other observability data formats.
• Experience in energy, utilities, manufacturing, or critical infrastructure domains.
• Understanding of edge computing and distributed data processing architectures.
• Experience with data mesh or data fabric architectural patterns.
• Knowledge of ML feature stores and data preparation for AI/ML pipelines.
• Exposure to graph databases (Neo4j, Amazon Neptune, Azure Cosmos DB Gremlin).
• Experience with regulatory compliance for data handling (SOC 2, NERC CIP).
• Certifications in cloud data platforms (Azure Data Engineer, AWS Data Analytics).

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