Summary

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Our client is modernizing how enterprise data, analytics, and AI capabilities are designed, delivered, governed, and adopted. 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. The role partners with Data Platform Engineering, AI, Product Management, and business teams to ensure data products are scalable, well-documented, cloud-ready, and tied to measurable business outcomes.

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Qualifications

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Education

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

\nExperience\n\n

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, including direct management of a team of 3–4 analytics engineers.
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.

\nSkills and Tooling Capabilities\n\n

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.
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, MicroStrategy, Tableau, or similar platforms.
Strong prioritization, communication, and stakeholder management skills across technical and business audiences.

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Responsibilities

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Lead and directly manage a team of 3–4 analytics engineers responsible for curated analytical data products, governed metrics, and reporting-ready assets.
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 duplicative assets, and ensure delivered data products create measurable business value.
Balance operational support with long-term product evolution, including reuse, consolidation, and retirement of duplicate analytical logic.
Serve as an escalation point for data trust, metric consistency, and production reliability issues.
Coach Analytics Engineers and Data Product Developers on modeling, documentation, engineering discipline, and product thinking.
Promote effective use of approved AI tooling, including Copilot, Vertex AI, and AI code assist capabilities, to accelerate delivery, improve documentation, and strengthen engineering practices while maintaining human review and accountability.

\nLeadership and Collaboration Expectations\n\n

Build trust through clear communication, follow-through, and transparent escalation of risks and decisions.
Create structure where ambiguity exists while respecting the expertise and ownership of technical teams.
Keep teams focused on reusable data products, business outcomes, data trust, and measurable value.
Help leaders understand where work is moving, where it is stuck, and what decisions or tradeoffs are needed.
Support a culture of accountability, shared visibility, disciplined engineering practices, and practical problem-solving.

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