Description

Design and deliver scalable, secure data pipelines and models that connect operational systems to analytics, ensuring trusted data for BI, ML, and automation solutions.

This role is on-site.

Responsibilities

• Build ingestion pipelines (batch, CDC, streaming) from S/4HANA, historians, and OT sources into landing, curated, and semantic layers.
• Implement data contracts, schema versioning, SCD handling, and performance tuning for file formats and caching.
• Develop dimensional and semantic models powering certified Power BI datasets and APIs for downstream apps.
• Integrate OT data via OPC UA/MQTT with strict safety protocols, collaborating with plant controls on signal quality and downtime.
• Automate build, test, and deploy processes using Git-based CI/CD, including environment approvals and blue/green deployments.

Required Skills

• 5+ years in data engineering building production pipelines at scale.
• Hands-on experience with Azure data stack: Databricks, Fabric/Synapse, ADF, ADLS/OneLake, Azure SQL/SQL MI.
• Strong SQL and Python/PySpark skills, with comfort in Spark Structured Streaming and performance tuning.
• Experience implementing data quality rules, unit/integration tests, and observability (freshness, schema, drift).
• Familiarity with SAP S/4HANA structures and SAP DataSphere semantic modeling.
• Knowledge of OT concepts: historians (PHD/PI), OPC UA/MQTT, event/batch frames, and ISA-95/99 basics.
• Understanding of Power BI consumption, including semantic models and Row-Level Security (RLS).

Preferred Skills

• Experience with time-series/data-quality tooling (e.g., Great Expectations) and feature/metric stores.
• Familiarity with MDM concepts, lineage/catalog tooling, and FinOps awareness.
• Domain exposure to TMS/WMS, LIMS, Historian, or HSE systems.

Key Skills

Education

Bachelor's degree