Manager Analytics

Caterpillar Inc.•Peoria, IL

2d•$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)
Knowledge of CI/CD pipelines and DevOps for data platforms
Technical leadership in data systems and quality engineering
Strong understanding of data governance and compliance frameworks
Analytical thinking and root cause problem solving
Stakeholder management and cross-functional collaboration
Ability to translate business needs into data quality KPIs and measurable outcomes

Responsibilities

Define and lead enterprise connected asset (Machines & Engines) data quality strategy aligned to business objectives and platform architecture.
Establish standardized data quality frameworks, rules, and metrics (completeness, accuracy, timeliness, consistency).
Design and implement scalable data validation, monitoring, and anomaly detection solutions.
Ensure integration of data quality controls within data pipelines, APIs, and platforms.
Partner with architecture teams to embed data quality by design in system development.
Drive adoption of AI/ML techniques for data profiling, anomaly detection, and root cause analysis.
Implement automated data quality checks within CI/CD and data pipelines.
Enable predictive data quality monitoring using telemetry, logs, and metadata insights.
Improve efficiency and coverage through automation and intelligent rule generation.
Build, mentor, and lead a team of data quality engineers and analysts.
Develop capabilities in data engineering, automation, and quality engineering practices.
Foster a culture of quality, accountability, and continuous improvement.
Manage resource planning, hiring, performance coaching, and career development.
Collaborate with software engineering, data engineering, analytics, and product teams to define quality requirements and SLAs.
Partner with program and platform teams to ensure data quality readiness for releases and NPI programs.
Drive issue resolution workflows and root cause analysis for data defects.
Communicate data quality health metrics and risks to leadership.