Manager, Analytics Engineering

Title:

Manager, Analytics Engineering

Location:

Katy, TX 77449 (ONSITE)
Duration:
Contract to Hire
Work Requirements: U.S. Citizen, GC Holders, or Authorized to Work in the U.S.

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. This role partners with Data Platform Engineering, Data Intelligence, Product Management, and business teams to ensure analytical products are scalable, well-documented, cloud-ready, and aligned to measurable business outcomes.

• Bachelor's degree in Computer Science, Engineering, Information Systems, Data Analytics, Business, or related field preferred.
• Equivalent years of relevant work experience may be considered.

• 6-8 years of experience in analytics engineering, data engineering, business intelligence engineering, data products, cloud data platforms, or related fields.
• 3+ years of experience leading analytics, data engineering, data product, or technical delivery teams.
• Experience leading or directly managing analytics engineers, data engineers, data product developers, or related technical roles preferred.
• Experience supporting governed metrics, curated analytical models, semantic layers, data quality controls, and production-grade reporting or AI-enabling datasets.
• Retail, ecommerce, merchandising, pricing, supply chain, customer, marketing, finance, or adjacent industry experience preferred.

• Strong understanding of analytics engineering, semantic modeling, governed metrics, and reusable data product design.
• Hands-on or leadership experience with data build tools (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, computing, security, performance tuning, cost management, and production operations.
• Experience establishing standards for effective analytics delivery, including dashboard design, metric presentation, self-service enablement, stakeholder usability, performance, documentation, and consistent quality expectations across BI tools.
• Ability to evaluate, adopt, and coach teams on responsible AI tooling practices that improve engineering productivity, documentation quality, model development, and code quality without compromising governance, security, or review discipline.
• Ability to translate technical delivery into clear business outcomes, risks, decisions, and next steps, including the ability to deliver stakeholder-facing analytics through business intelligence tools such as Power BI, Looker, Micro Strategy, Tableau, or similar platforms.
• Strong prioritization, communication, and stakeholder management skills across technical and business audiences.
• Ability to create structure where ambiguity exists while respecting the expertise and ownership of technical teams.
• Ability to communicate delivery progress, risks, tradeoffs, decisions, and next steps to leadership and stakeholders.
• Ability to build accountability, shared visibility, disciplined engineering practices, and practical problem-solving across teams.

• Lead and directly manage a team of analytics engineers responsible for curated analytical data products, governed metrics, and reporting-ready assets.
• Partner with Product Management to develop and execute roadmaps for Analytics Engineering capabilities, governed metrics, semantic models, and analytical data products.
• Own semantic consistency, model quality, documentation standards, and metric governance across analytics engineering pods.
• Establish delivery expectations, service levels, and operational standards for analytical data products.
• Oversee production readiness and operational reliability by ensuring clear monitoring practices, runbooks, issue triage processes, root cause analysis, stakeholder communication, and continuous improvement of data quality and freshness.
• Partner with business stakeholders, Product Management, AI, and Data Platform Engineering to prioritize data product needs, resolve tradeoffs, drive adoption, collect feedback, monitor usage, retire…