Manager, Analytics Engineering

Role Overview

The Manager, Analytics Engineering leads teams that transform governed data into trusted, reusable, business-ready analytical products. This role is accountable for semantic quality, governed metrics, analytical model reliability, documentation standards, and stakeholder confidence across curated data products and reporting-ready assets.

What You Will Do

Lead and directly manage a team of 3–4 analytics engineers responsible for curated analytical data products, governed metrics, and reporting-ready assets. Establish delivery expectations, service levels, and operational standards for analytical data products.

Why It Might Be a Fit

The ideal candidate has 6+ years in analytics engineering, data engineering, business intelligence engineering, data products, cloud data platforms, or related fields, and 3+ years leading analytics, data engineering, data product, or technical delivery teams.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Analytics, Business, or a related field
• 6+ years in analytics engineering, data engineering, business intelligence engineering, data products, cloud data platforms, or related fields
• 3+ years leading analytics, data engineering, data product, or technical delivery teams
• Experience supporting governed metrics, curated analytical models, semantic layers, data quality controls, and production-grade reporting or AI-enabling datasets
• Strong understanding of analytics engineering, semantic modeling, governed metrics, and reusable data product design
• Hands-on or leadership experience with dbt or similar transformation frameworks, advanced SQL, Git-based code management, CI/CD, testing frameworks, documentation standards, peer review practices, and approved AI-enabled productivity, engineering, and code assist tools
• Experience with cloud data platforms and cloud computing concepts across AWS, GCP, or Azure, including storage, compute, security, performance tuning, cost management, and production operations
• Ability to evaluate, adopt, and coach teams on responsible AI tooling practices
• Ability to translate technical delivery into clear business outcomes, risks, decisions, and next steps
• Strong prioritization, communication, and stakeholder management skills across technical and business audiences

Tagged as: Data Analysis, SQL