Manager, Data and Analytics

The team sits within Enterprise Analytics and owns the revenue data layer that the rest of the business depends on: merchant-level revenue reporting, product P&Ls, and the dashboards that land on executive desks each month. When this team works well, finance reconciliations close cleanly, product owners have reliable numbers to plan against, and account retention conversations are grounded in actual performance data. When it breaks down, revenue figures drift from financials, stakeholders build their own shadow spreadsheets, and trust in the data layer erodes.

This is a replacement hire, so the infrastructure and team exist. The work is not greenfield. The priority in the first year is tightening what is already there: reconciliation processes, data definitions, documentation, and the governance layer that keeps business logic consistent across reporting surfaces.

The role spans three modes. You will do hands-on analytical work, including SQL and data model review. You will manage a team of analysts, setting priorities and reviewing outputs. You will also represent the team to senior stakeholders, translating analytical findings into summaries that inform decisions at the executive level.

- SQL (advanced, including query optimization)
- Snowflake (performance and cost optimization)
- Power BI or Tableau (data modeling, dashboard delivery, governance)
- Relational databases and multi-source data environments
- Revenue data modeling and segmentation logic
- Exposure to Python or R (preferred, not required)

- Establish a monthly reconciliation process that closes the revenue data repository against financial statements with documented variance thresholds
- Define and publish consistent business logic for revenue models, merchant segmentation, and product P&L reporting, adopted across all active stakeholder groups
- Deliver a documented inventory of existing analytical models and reporting outputs, with validation status noted for each
- Own and improve the cadence of daily, weekly, and monthly dashboards, including at least one executive summary format reviewed and approved by the Senior Manager
- Audit current Snowflake usage and produce a written recommendation on at least one cost or performance optimization
- Complete a data governance review with the Enterprise Information Management and Business Intelligence teams, resulting in agreed data definitions and ownership assignments
- Build and maintain a team delivery roadmap aligned to Enterprise Analytics objectives for the fiscal year

A typical week runs on a rhythm set by the reporting calendar. Mondays often involve reviewing the prior week's dashboard outputs with the team, catching any data anomalies before they surface in stakeholder inboxes. Mid-week is usually the heavier analytical work block: reviewing SQL logic on a model a senior analyst is building, working through a Snowflake query that is running slower or more expensively than expected, or drafting the narrative for a monthly executive summary.

Thursday or Friday tends to carry the stakeholder-facing work. That might be a standing sync with the Business Intelligence team to align on a data definition that two product teams are currently interpreting differently, or a review meeting with the Senior Manager to walk through the revenue reconciliation before it goes to finance. Artefacts from these meetings are usually a revised data dictionary entry, a dashboard annotation, or a short written summary of a discrepancy and its resolution.

Team management is distributed across the week rather than concentrated in one block. One-on-ones with analysts, reviewing their work before it ships, and adjusting priorities when a business request comes in mid-sprint are regular parts of the schedule. The team is small enough that the manager is in the work, not just above it.