Lead Fleet Reliability Data Engineer

Build and maintain scalable data pipelines integrating diverse fleet data (telemetry, alarms, maintenance, software, interventions) for global Solar and Storage assets.

Develop and enforce standards and models for fleet asset hierarchy, data quality, traceability, and lifecycle event documentation to support reliability analysis and Root Cause Analysis.

Collaborate across engineering, analytics, and operations teams to deliver governed, reusable, and accessible data products enabling performance monitoring, predictive analytics, and decision support.

Bachelor's degree in Computer Science, Data Engineering, Software Engineering, Electrical Engineering, Systems Engineering, Control Systems Engineering, or related technical field.

Minimum 8 years experience in data engineering, industrial data systems, software engineering, reliability data, operational technology data, or related function.

Strong proficiency in SQL and Python; experience designing and operating ETL/ELT pipelines with multiple structured, semi-structured, and time-series data sources.

Experience implementing data quality validation, lineage, monitoring, error handling, reconciliation, and traceability controls.

Deep understanding of industrial assets and reliability engineering concepts including failure modes, Root Cause Analysis, corrective actions, and equipment lifecycle.

Experienced in industrial data environments such as SCADA, historians, operational telemetry, and with cloud data platforms like Azure, AWS, Databricks, or Snowflake.

Proven ability to deliver robust, documented data products/services enabling cross-team collaboration in complex engineering and operational settings.