Engineering Manager - Data Quality

Caterpillar Caterpillar · Industrial · Mossville, IL +1

Engineering Manager for Data Quality at Caterpillar, leading a team to ensure accuracy, integrity, consistency, and reliability of enterprise connectivity data. The role involves defining data quality strategy, establishing frameworks, designing validation solutions, and driving adoption of AI/ML for data profiling, anomaly detection, and root cause analysis. It also includes people management, cross-functional collaboration, and ensuring data quality for analytics, AI/ML models, and business decision-making.

What you'd actually do

  1. Define and lead enterprise connected asset (Machines & Engines) data quality strategy aligned to business objectives and platform architecture.
  2. Establish standardized data quality frameworks, rules, and metrics (completeness, accuracy, timeliness, consistency).
  3. Design and implement scalable data validation, monitoring, and anomaly detection solutions.
  4. Drive adoption of AI/ML techniques for data profiling, anomaly detection, and root cause analysis.
  5. Build, mentor, and lead a team of data quality engineers and analysts.

Skills

Required

  • Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering, or related field
  • 8+ years of experience in software/data engineering
  • 2–5 years in leadership roles
  • Strong experience in data quality
  • data governance
  • data engineering ecosystems
  • Hands-on experience with data pipelines
  • ETL/ELT frameworks
  • cloud platforms (Azure, AWS, or GCP)
  • data modeling
  • metadata management
  • data lineage tools
  • Experience implementing automated testing and validation frameworks for data systems

Nice to have

  • Experience with AI/ML-based data quality monitoring
  • Familiarity with streaming data platforms (Kafka, event-driven architectures)
  • Exposure to regulated environments (industrial, manufacturing, healthcare, finance)
  • Knowledge of CI/CD pipelines and DevOps for data platforms

What the JD emphasized

  • AI/ML techniques for data profiling
  • anomaly detection
  • root cause analysis
  • predictive data quality monitoring
  • AI/ML-based data quality monitoring

Other signals

  • AI/ML models
  • data quality strategy
  • data governance
  • automated validation pipelines
  • AI/ML techniques for data profiling
  • anomaly detection
  • root cause analysis
  • predictive data quality monitoring