Manager - Data Analytics(Data Insights Lead)

General Summary

Minimum Qualifications

• Bachelor's degree and 2+ years, associate's degree and 4+ years, or high school diploma and 6+ years of Engineering Operations or related experience.

Data Ecosystem and Analytics

• Review Engineering Operations datasets and implement data architecture, governance, pipelines, retention, and documentation.
• Partner with IT and Data Engineering to improve ingestion, quality, accessibility, standardization, and data hygiene.
• Analyze high-volume data on data center utilization, engineering job efficiency, infrastructure usage, and operational metrics.
• Build predictive models, forecasting frameworks, automated insight pipelines, and anomaly detection systems.
• Require dashboards, reports, and metrics to include actionable insights and recommendations.
• Analyze compute clusters, LSF, servers, and workflows to optimize resource planning, scheduling, usage, and cost efficiency.
• Partner with Engineering, Operations, HR, Facilities, Procurement, Program Management, and Finance on analytical decisions.
• Manage and mentor analytics professionals, review deliverables, and build a culture of curiosity and ownership.

Required Qualifications

• Bachelor's or master's degree in Data Science, Statistics, Computer Science, Engineering, or a related field.
• 10+ years of data science or data analysis experience, preferably in data center operations, engineering operations, or technology environments.
• Knowledge of machine learning, forecasting, anomaly detection, predictive analytics, Python or R, SQL, and advanced statistical methods.
• Deep experience with Power BI, Tableau, or comparable visualization tools.
• Ability to translate complex datasets into strategic insights and communicate effectively with technical and non-technical audiences.
• Experience managing complex analytical projects across multiple stakeholders with strong attention to data integrity.

Preferred Qualifications

• Experience with cloud platforms, server architecture, cluster scheduling systems such as LSF, and compute-heavy environments.
• Familiarity with engineering development workflows.
• Prior experience leading a team.

What Success Looks Like