Sales & Operations Analytics Engineer

Sales & Operations Analytics Engineer

(also fielded as: Analytics Engineer, BI Engineer, Commercial Analytics Analyst)

About turn

turn is a family-founded, independent brand operating across CA, AZ, NY, and FL. We run lean and we build with intention. This role exists because our reporting has to get dialed in, and we need one technical builder who can own the truth of our numbers end to end.

The mission

Own a single source of truth for sales and operations. Pull the raw reports from our distributors and distribution partners, compile them with COGS, and turn them into decision-grade reporting: revenue, collections, inventory, supply chain, sell-through, and sales velocity, account by account, trending 12 months.

You are the engine behind our Sales and Operations Planning (S&OP) process. You build the truth and sit in the room. You do not own the forecast. That call sits with Sales, Ops, and leadership. Your job is to make sure leadership is deciding from clean numbers, not fighting about whose spreadsheet is right.

This is a build role, not a maintain role. You are standing up the foundation, not inheriting a finished one.

Current state vs. where you take it

Today we have a strong sell-in reporting layer in Tableau: wholesale revenue by AE, by account, by category, by retailer, by strain, for CA and NY. That is the floor.

Where you take it:

• Extend the model to all four markets: CA, AZ, NY, FL. + D2C and new 2026/2027 markets
• Add the layer we do not have yet: sell-through and depletion. What the retailer actually sold off the shelf, not just what we shipped them. This is the hard part and the reason this role exists.
• Stand up inventory, supply chain, collections, and account-level P&L on the same foundation.

What you will own

The data foundation

• Ingest distributor and distribution depletion, shipment, and inventory reports across multiple partners and multiple formats, via API wherever possible, automated wherever possible.
• Integrate sell-through from the platforms and partners that actually have it: Headset, Pistil Data, Sparkplug, and the retail chains that share their own POS (Catalyst and others).
• No one in cannabis has 100% sell-through. Assemble the best available coverage and label clearly what is true sell-through versus a sell-in proxy. Sell-in is internal and clean. Sell-through is external and partial, and stitching it together is the whole reason this role exists.
• Standardize the mess into one model: SKU mapping, account mapping, category and unit normalization across all four states.
• Build and own the data model everything else reads from. One source of truth, not a pile of spreadsheets.

The reporting layer

• Inventory on hand, what is incoming, and what is in production.
• Revenue, and collections (AR aging), pulled on a reliable cadence from Finance.
• Sales history by SKU by month, account by account.
• Sell-through, sales velocity, and in-stock vs. out-of-stock at the account level.
• A weekly sales view: velocity and out-of-stock by account, with each rep able to pull their own book.
• Account-level P&L, quarterly to start, focused on revenue and net margin, with COGS tied in.
• 12-month trending across every metric above, at the same granularity the current scorecard runs: state, AE, account, retailer, category, strain.

Commissions

• Run the monthly and quarterly sales commission calculations. Work with the reps to resolve every discrepancy, then hand a clean, final number to accounting for check and approval. Automate the calc so it stops being a monthly fire drill.

Reconciliation

• PO vs. co-packer close-out. Expected quantity against actual, with over, under, waste, and damage broken out alongside supply chain.
• Sell-in vs. sell-through, so we can see where product is shipping but not moving.

Feeding S&OP

• Build and ship the monthly S&OP pack: end-of-month capacity, what was planned to be brought in, what was actually brought in, what was actually sold, and what is carrying over.
• Sit in the planning conversations as the data owner. Pressure-test assumptions. Surface variance early enough to act on, not explain after the fact.

First 90 days (in this order)

1. Map every data source, owner, and format. Document where the data is dirty and why.
2. Stand up the internal foundation first. Automate the highest-volume distributor feeds and get inventory (on hand, incoming, in production) and sell-in reporting clean and trusted across markets. This is the controllable, fast part.
3. Ship one dollar-tied win early. A stockout and reorder-gap report on top accounts, so sales can recover revenue that is leaking right now. Money, not activity.
4. Then attack sell-through. Crack it end to end for one market, then scale the pattern to all four.
5. Define and ship the first monthly S&OP pack with Sales, Ops, and Finance.

Must-haves

• SQL, strongly preferred and tested in the interview. You can pull and shape your own data and build your own models, or show us you can get there fast.
• Mastery of Tableau, or Power BI, Looker, or Sigma. The tool is not the constraint, the skill is. We are open to a Tableau alternative if you make the case.
• Real ETL and data pipeline experience. API integration. Scripting in Python or equivalent to automate ingestion and cleanup.
• Data modeling discipline. You build a foundation, not one-off reports.
• Proof you have turned messy, multi-source external data (depletion, POS, or third-party platform feeds) into a trusted, decision-grade number, and been honest about coverage gaps rather than papering over them. We will ask you to walk through a specific example. If your examples are all internal sell-in dashboards, this is not your role.
• Commercial or CPG analytics background. You have lived in distributor and retail data and you know how messy it really is.

Nice-to-haves

• Cloud data warehouse experience (Snowflake, BigQuery, or similar) and dbt.
• Cannabis, hemp, beverage, or other regulated-distribution experience. Metrc and distributor-portal data, not just clean enterprise feeds.
• Hands-on with cannabis retail data platforms (Headset, Pistil Data, Sparkplug) or the equivalent in another regulated category.
• Financial literacy: COGS, margin, P&L structure, AR and collections. Helpful, but we can teach what you need for this role.
• Experience taking reporting from zero to one inside a fast, lean company.

How we will know you are winning

• Leadership trusts the numbers without re-checking them by hand.
• We can answer “what is selling through, where, and how fast” on every account we have coverage for, in minutes not days, and we know exactly which accounts are still on sell-in proxy.
• Sell-in and sell-through are reconciled, so we can see exactly where product is shipping but not moving.
• The monthly S&OP pack is automated, on time, and decision-grade.
• All four markets run on the same foundation.

Who you will work with

You work across Operations, Sales, and Finance, and report into Sales to start. Sales owns the reporting need today, and this may evolve as the analytics function grows. You sit at the intersection of all of them, which is exactly why this role matters.

Compensation

Base salary in the range of $130K to $160K, dependent on experience and level. Performance bonus tied to clear deliverables. Real growth path as the data function scales.

the practicals.

Location. Hybrid Work Environment. The better the data, the less need for in-office.

Hours. We work overtime until the books are dialed in. We almost never need middle of the night calls.

Benefits. Health and dental coverage. Standard PTO.

how to apply.

Apply at www.turn.me/apply or send your resume directly to [email protected].

a note from the founders.

We started turn in 2021 to change how people see cannabis. Red Bull, Supreme, Off-White, Poppi. Experience first. Sometimes the emotional ROI matters more than the spreadsheet ROI, and we live that way.

If you execute, you earn trust, growth, and a long-term seat on the team that is building this thing.