Golden Data

JD Case Study · Salomon

Senior Manager, Sales Operations, Data, Analytics & Forecasting

Where
Ogden, UT · hybrid
Pays (est.)
$130K–$175K + bonus (est.)

Salomon is not hiring a dashboard-maintainer. The posting calls this seat the operational and analytical backbone of the North America sales organization and hands it the seasonal forecast, the wholesale order book, the Power BI layer every function reads, and a team. The job is to make a seasonal book legible: one forecast everyone trusts, and a miss traced to the plan, the sell-in, the fill or the floor.

One kind of number here. The analysis below runs on illustrative figures invented to show method. No Salomon data is used or implied; the operating logic is the deliverable.

Aug 13, 2026

What they’re actually buying

Own the number the sales org runs on

Strip the title down and the mandate is: own the number the North America sales org runs on, and the system that produces it. Consolidate forecasts across accounts, channels, and categories; track them against plan, budget, and seasonal targets; keep the Power BI layer — data model, DAX measures, row-level security — accurate enough that each account leader trusts their own book and Finance trusts the total. Administer the sales platforms, ready the org for the CRM and B2B rollouts coming, and build the analytics-and-planning team that carries all of it. The hard part is not the DAX. It is that in a seasonal wholesale business, ‘why did we miss?’ has four honest answers held by four different functions — the forecast, the sell-in, the fill rate, and the sell-through — and the role only works when one reconciled number makes Sales, Planning, Finance, Customer Service, and Supply Chain argue about the fix instead of the figure.

The first 90 days

Reconcile, ship the layer, run one season

The order matters. Nobody should change a system they haven’t reconciled, and nobody should present a verdict before the numbers under it can be trusted.

Days 1–30

Listen, then reconcile the definitions

Sit with the Director of Sales Operations, the key account leaders, and each cross-functional partner the JD names — Planning, Finance, Customer Service, Supply Chain. Inventory every forecast, scorecard, and dashboard already in use and where each number is sourced. Audit the reporting stack end to end: the SPS and Snowflake feeds, the Power BI data model, the DAX measures, the row-level security, and the known breaks. Reconcile forecast, order-book, and sell-through definitions across accounts, channels, and categories until one number survives every review — and meet the team I’d be inheriting and map who owns what.

Days 31–60

Ship the governed forecasting & reporting layer

Rebuild the executive scorecard and dashboard set on governed metric definitions, with lineage from SPS and Snowflake source to Power BI tile and row-level security so every account leader sees their own book and only their own. Stand up the consolidated forecast across accounts, channels, and categories from one model, with performance tracked against plan, budget, and seasonal target by construction rather than by hand. Route recurring production to the team with definitions and QC checks attached — coaching the analysts, not clearing their queue.

Days 61–90

Run one seasonal cycle properly — and present it

Take the quarter’s flagship review — a pre-season sell-in or an LRP input with the key account leaders — and wrap it in method: an order-book health read, inventory and ATS positions, door-level sell-through, and business risks with dollars attached rather than adjectives. Size the top two account or category growth opportunities as business cases, and present both versions to platform leadership: the deep-dive and the one slide. The point is to leave behind a repeatable seasonal cadence, not a one-time deck.

Signature analysis · illustrative data

Signature analysis: the plan-to-sell-through funnel

The measurement move: never let a seasonal miss stay one blended number against plan. Decompose it — did we plan wrong (forecasting), did the account not book it (sell-in), did we not ship it (fill rate and ATS), or did it not move at the door (sell-through)? Each drop has a different owner — Sales Ops, the key account, Supply Chain, the field — and a different fix. A wholesale book reported on that decomposition is how five functions argue about the action instead of the number, and how next season’s forecast gets better instead of louder.

Where between the plan and the floor does a season go missing?Indexed to the first stage

Seasonal plan100indexed to 100 — the pre-season LRP target
Booked on the order book88
Shipped to the door74net of cancellations and what ATS could fill
Sold through at retail52
Reordered / replenished30the demand signal that sets next season’s plan
Illustrative figures to demonstrate the method — a wholesale plan-to-sell-through funnel. No Salomon or account-confidential data is used or implied; the operating logic is the deliverable.

Requirement → proof

The posting, answered line by line

Each line of the posting against something already built and running on this site. Where the fit is a ramp rather than a match, the row says so.

What Salomon asks forWhat I’ve already shipped
Consolidate and maintain sales forecasts across accounts, channels, and categories; forecasting and demand planningA forecast-coverage model that reconciles the number promised to leadership with the funnel underneath it — the same forecast-versus-reality discipline, with the math shown under the chart.
Track performance against plans, budgets, and seasonal targets; analyze order-book health and business risksA capacity model that separates committed, ramping, and realized volume and traces each gap back to a dollar figure — plan-versus-actual decomposed, not blended into one variance.
Dashboards, scorecards, and automated reporting; Power BI data modeling, DAX measures, and row-level securityA governed dbt layer running live on this site: tested definitions, source-to-tile lineage, and a leak gate that blocks the publish when a definition breaks or a row leaks — the Power BI equivalent of data-model integrity and row-level security, enforced as CI instead of a checkbox.
Support executive reporting and business reviews for a leadership audienceExecutive growth scorecards and market-intelligence briefs built to surface the action, written for the room rather than the analyst.
Deliver recommendations to improve commercial performance; test-and-learn on data-driven initiativesAn A/B and incrementality readout with confidence intervals and a sample-size calculator — a recommendation you can size in dollars before you ship it.
Build and develop a high-performing Sales Analytics and Planning team; coordinate delivery and process complianceAn agent-run delivery fleet with schedules, QC gates, and a freshness dead-man’s-switch — the operating discipline of running an analytics org (definitions, cadence, a quality bar) that transfers straight to a human team.
Experience within a wholesale, consumer goods, footwear, apparel, or outdoor brand environmentThe honest one: my book was consumer finance, not wholesale apparel — funnel, forecast, and portfolio analytics calibrated to filed results. The forecasting, governance, and decomposition transfer directly; the sell-in, sell-through, and seasonal-buy specifics of an outdoor book are my first-season ramp, and I’d say so to the Director in week one.

The same engine

Every requirement above runs on one engine — consolidated forecasting, full-funnel decomposition, governed metric definitions with a leak gate, experimentation, and executive scorecards — and I have already shipped it in public on a consumer-finance book. Point it at a wholesale order book instead of a loan portfolio and the machine is the same: the funnel gets a seasonal plan at the top, the leak gate guards the Power BI layer, and every account leader — and Finance — reads one number that means one thing. The merchandise changes; the measurable path from plan to sell-through does not.

See the forecast-coverage model →

The hard part of this role isn’t the Power BI or the Snowflake — it’s making five functions and a room full of account leaders call a seasonal book off one number, months before the sell-through proves anyone right. That’s a forecasting-and-governance problem before it’s a reporting one, and this page is the free sample of how I’d run it — from my own backyard in Ogden. — Paul Brown