JD Case Study · Salomon
Senior Manager, Sales Operations, Data, Analytics & Forecasting
Salomon is not hiring a dashboard-maintainer. The posting says it plainly — this person is the operational and analytical backbone of the North America sales organization — and then hands them the seasonal forecast, the wholesale order book, the Power BI layer every function reads, and a team to run it. It is one seat sitting between the five orgs the JD names by name — Sales, Planning, Finance, Customer Service, and Supply Chain — in a business that commits inventory a season ahead and has to call a highly seasonal book before the snow does. The job is to make that book legible: one forecast everyone trusts, order-book health you can decompose, and door-level sell-through that says whether a miss came from the plan, the sell-in, the fill, or the floor.
What they’re actually buying
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.
How I’d own it — first 90 days
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: 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.
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 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