Manager, Business Intelligence

Topgolf•Dallas, TX

About The Position

The Manager, Business Intelligence leads a team of product owners and reporting engineers who build and maintain the reporting our corporate business areas and venues run on. You set the standard for how data is modeled, defined, and delivered in Amazon Quick Suite (Quick Sight) on top of Redshift and Snowflake, and you connect two halves of the team: the product owners who own the relationship with the business and the backlog, and the engineers who build against it. Success is measured less by dashboards shipped and more by decisions improved — how fast a venue GM or a corporate leader gets a reliable answer, and how much they trust it.

Requirements

5+ years in business intelligence, analytics, or analytics engineering, including 2+ years directly managing people.
Experience leading a mixed team of technical builders and business-facing product or business analyst roles, and coaching each toward different definitions of good work.
Working fluency in agile delivery — backlog management, prioritization frameworks, sprint or release cadence, and stakeholder demos.
Hands-on experience administering and developing in Amazon Quick Suite / Amazon QuickSight — datasets, SPICE, row-level security, and permissions — not just building charts. (Comparable deep experience in Tableau, Power BI, or Looker with a clear willingness to own Quick Suite will be considered.)
Advanced SQL with performance tuning experience against Redshift and/or Snowflake at scale.
Strong dimensional data modeling skills and a track record of building reusable, well-documented models.
Demonstrated ability to influence senior stakeholders and translate complex analysis into plain-language recommendations.
Proven success influencing without authority across technology teams — securing reporting requirements in someone else's project plan, and holding upstream partners to data contracts and change notification.
Experience owning a departmental or technology budget, including annual planning, invoice review, and forecasting consumption-based cloud spend.
Working knowledge of data privacy and access-governance principles.
Bachelor's degree in Business, Analytics, Computer Science, Statistics, Economics, or equivalent practical experience.

Nice To Haves

Experience with the newer Amazon Quick Suite AI capabilities — Quick Sight Q / natural-language Q&A, Quick Flows, Quick Automate, Quick Research.
Experience using AI tools and LLM assistants to accelerate BI work — generating and reviewing SQL, drafting data models, building or documenting dashboards, and automating recurring analysis.
Familiarity with MCP (Model Context Protocol) servers and connecting AI assistants directly to data warehouses and BI platforms to enable conversational analytics.
Familiarity with software capitalization concepts for internal-use software and cloud computing arrangements (ASC 350-40 / ASU 2018-15) and how they apply to analytics work.
Experience with FinOps practices and cloud cost management tooling (AWS Cost Explorer, budgets and tagging, Snowflake resource monitors).
Experience with dbt or a comparable transformation framework, and Git-based development workflows.
Python for analysis, automation, and Quick Suite asset management via the AWS SDK/CLI.
Broader AWS familiarity — S3, Glue, Lambda, IAM, Step Functions.
Experience with orchestration tooling (Airflow, Dagster) and modern ELT stacks.
Exposure to experimentation/A-B testing, forecasting, or predictive modeling.
Background in multi-unit restaurant, hospitality, entertainment, or retail — particularly venue-level P&L, labor, and guest analytics.

Responsibilities

Lead, coach, and develop a team of BI product owners and reporting engineers; own hiring, onboarding, performance, and career growth across two distinct skill tracks.
Develop product owners in requirements gathering, backlog management, stakeholder communication, acceptance criteria, and saying no well.
Develop reporting engineers in SQL, data modeling, Quick Suite development, and engineering craft.
Hold the line between the two: ensure requirements arrive well-defined and that engineers aren't absorbing unscoped work mid-sprint.
Run the team's delivery cadence — intake, prioritization, sprint or release planning, and demo/review — balancing executive asks against roadmap commitments and technical debt.
Establish and enforce team standards for SQL, data modeling, version control, code review, testing, documentation, and dashboard design consistency.
Own the BI product roadmap across corporate functions and venue operations; make the trade-off calls when demand exceeds capacity.
Ensure each business area — Finance, Operations, Marketing, F&B, Guest Experience — has a clear product owner relationship and a visible backlog.
Define what "done" means for a report: validated against source, documented, performance-tested, adopted, and supported.
Balance the two audiences the team serves: corporate stakeholders who need depth and cross-venue comparison, and venue leaders who need a small number of fast, unambiguous operational views they can act on during a shift.
Manage the full lifecycle of reporting assets — intake, build, release, adoption tracking, iteration, and retirement.
Build durable working relationships with Data Engineering, Application Development, Product, Infrastructure, and Security, and represent reporting's interests in their planning and design forums.
Get the BI team into the room early: participate in architecture and design reviews, intake for new systems and integrations, and vendor selection, so that data access, event capture, and reporting requirements are defined before build rather than retrofitted after launch.
Establish data contracts and change-notification expectations with upstream system owners so schema and vendor changes don't silently break downstream reporting.
Advocate for the instrumentation and source-system data quality needed to answer the questions the business actually asks.
Ensure reporting requirements and BI capacity are represented in technology roadmap and release planning, and that BI work is sequenced alongside — not behind — the projects that generate it.
Partner with Security and Compliance on access models, data classification, and audit requirements for BI content.
Own the Quick Suite environment end to end: datasets, SPICE refresh strategy and capacity, dashboards, analyses, folders, and row-level security.
Design governed, certified datasets that connect cleanly to Redshift and Snowflake, choosing appropriately between direct query and SPICE based on volume, freshness, and cost.
Administer users, groups, namespaces, and permissions; manage asset promotion across dev/test/prod and version control of Quick Suite assets via the AWS APIs/CLI.
Manage embedded analytics where BI content is surfaced inside internal applications and venue-facing tools.
Monitor and manage Quick Suite and warehouse consumption costs.
Own curated, well-documented data models in Redshift and Snowflake that serve as the single source of truth for core business metrics.
Partner with Data Engineering on pipeline reliability, data freshness, query performance, warehouse sizing, and cost.
Maintain a governed metric layer so that key measures — same-venue sales, revenue per bay hour, attach rate, labor percentage, guest satisfaction — are defined once and used consistently everywhere.
Reconcile and integrate data across source systems including POS, reservations and bay management, labor and scheduling, ERP/financials, CRM and loyalty, marketing platforms, and guest survey tools.
Deliver recurring reporting and ad hoc deep dives spanning the full Topgolf operating picture: Financial — revenue by stream, same-venue performance, margin, venue-level P&L, budget vs. actual and forecast variance. Operational — bay utilization and throughput, wait times, reservations and walk-in mix, event and group bookings, labor hours and productivity, daypart and seasonality analysis. Guest experience — survey and NPS results, review and social sentiment, complaint and service-recovery themes, membership and loyalty behavior, repeat visitation and cohort retention. Food & beverage — menu mix and item performance, attach rate and check average, COGS and waste, pricing and promotional lift, kitchen throughput. Marketing & growth — campaign performance, channel attribution, promotional ROI, new vs. returning guest acquisition. Venue and portfolio comparison — ranking and benchmarking across venues, new-venue ramp curves, and market-level performance.
Own the annual budget for the BI team's software, licensing, cloud consumption, and professional services; build the plan, defend it in the budget cycle, and manage to it through the year.
Review and approve monthly invoices for Quick Suite, Redshift, Snowflake, and supporting tooling; validate charges against contracted rates and expected consumption, and resolve billing discrepancies with vendors.
Apportion spend between capital and expense in partnership with Finance and Accounting, applying the company's capitalization policy for internal-use software and cloud implementation costs; provide the supporting documentation and effort allocations required to substantiate treatment at close and in audit.
Forecast consumption-based spend and flag variances early; explain month-over-month movement in cloud and licensing costs.
Drive cost efficiency through query optimization, SPICE and warehouse right-sizing, license true-ups, and retirement of unused tools and assets.
Support vendor renewals and negotiations with usage data and business justification; partner with Procurement and Legal on contracting.
Build and maintain a curated dashboard portfolio; retire redundant and low-usage assets as aggressively as you create new ones.
Enable business users — including venue leadership — to answer routine questions themselves through certified datasets, training, and clear documentation.
Track and report on BI adoption, usage, and data quality.
Own data quality monitoring, issue triage, and root-cause resolution for BI assets.
Ensure appropriate access controls, PII handling, and compliance with applicable privacy regulations (e.g., GDPR, CCPA).
Maintain clear data lineage and documentation so stakeholders understand where every number comes from.
Identify and automate manual reporting processes.
Evaluate emerging tooling — including AI-assisted analytics and natural-language querying — and pilot what delivers clear business value.