JD Case Study · Gong

People Analytics Manager

Gong sells an AI operating system that makes revenue teams legible to their leaders. This role is the same product, pointed inward: the analytical layer between Gong’s workforce data and the decisions its leadership team makes about people. The posting names the whole seam — Workday, CultureAmp, and Greenhouse as sources; dbt and Snowflake as the stack; dashboards, self-serve reporting, and workforce-planning models as the products; and the responsible adoption of AI as the charter. That is not a reporting job. It is a build-the-layer job, and the layer is exactly what my People Analytics teardown argues companies should buy as a product, not a suite.

Austin · Chicago · NYC · Salt Lake City · SF · $115K–$175K · View the job description →

Aug 12, 2026

What they’re actually buying

Strip the title down and the mandate is: turn three HR systems into one set of numbers the leadership team trusts. Governed definitions across Workday, CultureAmp, and Greenhouse so headcount and attrition mean one thing in every deck; analytical products stakeholders reach for unprompted instead of queueing for an analyst; data narratives an executive can retell; and an AI-adoption program that automates the workflow without ever automating away the trust. The hard part is named right in the posting — data quality gaps and data governance — because people data is the most sensitive data a company holds, and adoption dies the first time a number leaks or two decks disagree.

How I’d own it — first 90 days

Days 1–30

Listen, then reconcile the definitions

Sit with the Director of People Analytics, People Ops, and the Systems team; inventory every question leadership actually asked last quarter and which ones died in an analyst queue. Audit the Workday, CultureAmp, and Greenhouse feeds into Snowflake — ownership, refresh cadence, known breaks. Reconcile headcount, attrition, and requisition definitions until one number survives every deck, and map the privacy boundary: which fields may never leave the People team, and what enforces that today.

Days 31–60

Ship the governed layer and the first products

Stand up dbt models over the HR sources with tests attached — not_null, accepted-range, and an allow-list gate on anything that leaves the warehouse, so a definition break or a PII leak blocks the publish instead of shipping it. Rebuild the leadership dashboard set on those governed definitions with lineage from source to tile, and ship one self-serve product for the single most recurring stakeholder ask, so the queue starts shrinking by construction.

Days 61–90

Automate one workflow responsibly, and measure it

Pick one recurring reporting workflow and hand it to automation with QC gates, a freshness check, and a rollback — then measure agent-versus-human on cost, latency, and error rate, published as a scorecard rather than claimed in a deck. Close the quarter by delivering the first people-analytics narrative to the leadership team: three decisions the data argues for, each with a number, a confidence, and an owner.

Signature analysis: the question-to-decision funnel

Workforce questions leadership asks100indexed to 100 — a quarter’s worth

Answerable from the source systems78

Survive definition reconciliation55one headcount number, every deck

Answered self-serve, no analyst queue34

Land as a documented decision22the number that funds the function

The product move: treat people analytics as a product with a conversion funnel, not a queue with a backlog. Every drop-off has a different owner — systems integration, metric governance, product design, narrative craft — so the roadmap the posting asks for falls straight out of the decomposition: fix the biggest drop first, and report the funnel itself to leadership as the function’s own health metric.

Illustrative figures to demonstrate the method. No Gong or employee-confidential data is used; the operating logic is the deliverable.

Requirement → proof

What the JD asks for	What I’ve already shipped
dbt, Snowflake — and robust data governance over sensitive people data	A governed dbt layer running live on this site: anonymizing models, tested definitions, and a leak gate that blocks publishing when a PII pattern or a broken derivation shows up.
Analytical products stakeholders trust and actively use — dashboards, self-serve reporting	A self-serve salary explorer over a hand-verified openings dataset — a live workforce-data product, not a mock.
Workforce planning models	A capacity model that separates staffed, ramped, and productive headcount, then traces each gap into dollars — workforce planning with the math shown.
Data narratives for senior leadership — technical and non-technical audiences	Workforce economics told as narrative: fifty years of the productivity–pay gap, every figure EPI-sourced, written for the room rather than the analyst.
Responsibly adopt AI and ML to scale analytics and automate workflows	An agent-run delivery fleet with QC gates, schedules, and a freshness dead-man’s-switch — plus a published agent-vs-human scorecard measuring the automation instead of asserting it.
Data privacy and compliance requirements	The leak gate’s allow-list design: a column is invisible publicly until a model explicitly selects and reduces it, and a red test blocks the publish. Privacy as CI, not policy.
Experience with HR data ecosystems — Workday, CultureAmp, Greenhouse	A competitive teardown of UKG Pro People Analytics and the buy-the-layer-not-the-suite case built against it — the domain map this role operates on, published before the interview.

The same engine

Every requirement above runs on one engine — governed dbt definitions, a leak gate on sensitive data, self-serve analytical products, agent-run automation with QC gates, and narratives built for the room. I have already shipped it in public over the most sensitive dataset I have: my own. Point it at Workday, CultureAmp, and Greenhouse instead and the machine is the same — the funnel gets a workforce question at the top, the leak gate guards the people data, and leadership gets one number that means one thing.

Read the People Analytics teardown →

Hire the work, not the headcount

You’ve already priced this role. Here’s what the money buys.

Gong says People Analytics Manager is worth $115K–$175K a year (posted base range). Fully loaded — payroll taxes, health insurance, benefits, roughly +25% — the midpoint runs about $181K a year: $87 an hour, for all 2,080 hours, ramp and meetings included. And the ramp is real: a new leader’s breakeven averages about 6 months (Watkins, The First 90 Days) — call it $91K spent before the first shipped thing. The candidate who interviews well will be great in the room and diligent on the kanban board. What they won’t do is build the systems. That’s the part I sell.

The build · 90 days

$30,000

~120 hours at $250/hr ships the first-year plan on this page as working systems on your data — dashboards, pipelines, automations that keep running after I stop billing. You buy the 120 hours that ship, not the 2,080 you’d pay a hire for. Value in week one, not month 9; compare $91K for the hire’s ramp alone.

The bench · monthly

$3,000/mo · 10 hrs

10 hours of me a month, plus my agents running your data refreshes on a schedule — the same fleet that keeps this site current — evals green, costs flat, and me on call. Month to month; cancel anytime; the systems are yours either way.

Prepay the quarter and it’s $7,500 for 3 months — $1,500 off the $9,000 month-to-month price, for paying up front.

Founding-build rate: the first 3 builds run $15,000 — half the list price — in exchange for a named, written case study — all 3 still open. Email me to take one; when the third case study ships, so does this rate.

Or keep the req. Hire the person who’s great in the room — and pay me to make them dangerous: the same $30,000 build, handed to your new director on day one, pulls their breakeven from month 6+ to month two. The expensive mistake isn’t the hire; it’s the hire with no system underneath them. This case study is the free sample of what the system looks like.

Email me the req All services & prices

The hard part of this role isn’t the dbt or the Snowflake — it’s that people data is the one dataset where a single leak or one pair of disagreeing decks ends adoption for a year. That’s a governance-first build, and this page is the free sample of how I’d run it. — Paul Brown