Engineering Manager – Data Quality

Caterpillar Inc.•Mossville, IL

22h•$147,760 - $240,110•Onsite

About The Position

The Engineering Manager – Data Quality leads a high-performing team responsible for ensuring the accuracy, integrity, consistency, and reliability of enterprise connectivity data across platforms and products. This role combines technical leadership, people management, and data governance ownership to enable high-quality, trusted data that supports analytics, AI/ML models, and business decision-making. The manager collaborates closely with software engineering, data engineering, analytics, product management, telecom and governance teams to establish scalable data quality frameworks, automated validation pipelines, and compliance-aligned processes across the data lifecycle.

Requirements

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

Nice To Haves

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)
Data Quality Strategy
AI-Enabled Data Quality & Automation
Knowledge of CI/CD pipelines and DevOps for data platforms

Responsibilities

Lead the data quality strategy for connected asset data across enterprise platforms and products.
Establish standard data quality frameworks, rules, and KPIs such as accuracy, completeness, timeliness, and consistency.
Design scalable validation, monitoring, and anomaly detection solutions for enterprise data.
Embed data quality controls into pipelines, APIs, and platform architecture from the start.
Partner with architecture teams to ensure data quality by design in system development.
Drive AI/ML adoption for data profiling, anomaly detection, and root cause analysis.
Implement automated quality checks within CI/CD and data pipelines to improve efficiency and coverage.
Lead and develop a team of data quality engineers and analysts, building strong technical and quality capabilities.
Promote a culture of quality, accountability, and continuous improvement through coaching and people leadership.
Collaborate across engineering, analytics, product, and platform teams to define requirements, support releases/NPI, resolve defects, and communicate quality health and risks to leadership.