ABOUT OUR CLIENT

Our client is a high-growth direct-to-consumer health and wellness brand operating a digital-first model that pairs proprietary intake flows, a clinical care network, and a growing footprint of retail and studio touchpoints. Their core treatment is a one-time purchase with a subscription tail, and the business has scaled to a meaningful eight-figure run rate with strong cash discipline.

The data stack runs on Microsoft Fabric on top of the Microsoft SQL ecosystem, with source data flowing in from Shopify, Salesforce, Salesforce Marketing Cloud, paid media platforms, and a growing list of operational systems. The company has built a custom architecture that feeds data through Marketing Cloud into Fabric and out to a Power BI presentation layer, plus a Claude Enterprise environment with a working MCP server and a skills / data dictionary layer that the team uses heavily for insight generation.

The environment is honest. Some of it is clean, some of it needs a fixer, and the BI layer specifically has been chasing the warehouse. If you get energized by turning that into clarity, and if you want to own the analytics function at a company where data actually changes daily decisions, this role was written for you.

THE OPPORTUNITY

This is a hands-on, operator-first role. You will be the primary architect of how the business measures itself, from top-of-funnel paid media through purchase, retention, and lifetime value. You will write SQL, build dashboards, own the BI layer, and shape the data architecture so that other tools (including LLM-powered ones) can consume it. You will inherit a small team but you are being hired to operate, not to manage headcount.

You will join at a critical inflection point, helping move the business from a warehouse full of raw potential to a reliable, insight-generating data platform that leadership trusts and uses every day.

WHAT YOU WILL OWN

Data Platform & Architecture

• Own the Microsoft Fabric warehouse end-to-end, including schema governance, pipeline quality, table deprecation, and data freshness.
• Make the right architectural calls on where logic lives. Keep transformations in the data layer (not buried in BI) so the same definitions can be consumed by Power BI, by Claude via MCP, and by downstream operational use cases.
• Partner with the ETL pipeline owner and the engineering team on transformation standards. Partner with Shopify, Salesforce, and marketing tool owners on upstream data contracts.
• Establish documentation, data dictionaries, and naming conventions so every table and every metric is unambiguous.

BI Layer & Executive Reporting

• Own Power BI as the visible layer for the business. Rebuild trust in the dashboards by fixing refresh consistency, layout coherence, and metric definitions.
• Design dashboards that read like an executive needs to read them. The nomenclature should be the company’s nomenclature, the layout should reveal the answer, and someone seeing it for the first time should understand what they are looking at.
• Own the weekly executive reporting framework and evolve it toward self-service over time.
• Be in the room for daily and weekly leadership cadences. You will have a direct line to the CEO, Chairman, and Head of Technology, and your work will be a cornerstone of how the company is run.

AI, MCP, and LLM-Powered Analytics

• Extend the existing Claude Enterprise + MCP server setup. The company already has a working MCP server, data dictionary, and skills library that feeds the LLM accurate context. You will own and grow that surface.
• Shape data and views with LLM consumption in mind. Build queries and logic that an AI tool can use to provide reliable findings against the data warehouse, not just visuals a human can read.
• Help drive the roadmap toward open-source LLMs running on-prem as the business scales, to control cost and keep proprietary data inside our walls. If you have hands-on experience building or shaping LLMs, that is a real plus.

Marketing, Funnel & Revenue Analytics

• Own end-to-end paid media analytics across Meta, Google, and affiliate channels. Spend pacing, ROAS, CAC by channel, contribution margin by campaign.
• Build and maintain multi-touch attribution. Reconcile platform-reported data against warehouse actuals to produce a single source of truth for media performance.
• Instrument and own the full acquisition funnel from paid click through lead qualification, evaluation, purchase, and fulfillment. Make sure every step is consistently measured and trustworthy.
• Partner with marketing on test-and-learn frameworks: holdout testing, incrementality, A/B analysis. Track and close signal loss from iOS ATT and cookie deprecation.
• Build the canonical metric definitions for the business: blended and channel-level CAC, LTV, AOV, primary-product-to-subscription conversion, subscription retention, and email / SMS program performance.
• Lead deep-dive analyses on retention, churn, subscription lifecycle, and upsell. Surface the highest-leverage levers for revenue growth.

Team & Stakeholder Leadership

• Inherit and develop a small existing team (one full-time analyst, one part-time data analytics intern, and one part-time marketing contractor).
• This is an operator role, not a headcount-growth role. The goal is to keep the team lean and use tools and AI to scale output, not bodies.
• Serve as the primary analytics partner to the CEO, Chairman, Head of Technology, Marketing, CRM, Clinical, Finance, and Operations leaders.
• Translate business questions into data projects with clear scope, timelines, and outputs. Present findings to executive leadership in plain language, with so-what recommendations, not just charts.

WHAT YOU WILL WALK INTO (90-DAY PRIORITIES)

We believe in transparency. This is a build and fix role — you will inherit a warehouse with real potential and real gaps. Here is where you will focus first:

• Data platform audit: Evaluate the current warehouse architecture, surface quality issues, and establish a remediation roadmap with clear priorities and owners
• Attribution & paid media clarity: Reconcile cross-channel attribution across Meta, Google, and affiliate — establish a single source of truth for CAC, ROAS, and channel-level contribution margin
• Funnel instrumentation: Assess coverage and accuracy of the full acquisition funnel from first touch through purchase, ensuring every conversion step is measured, named consistently, and trustworthy
• Data quality remediation: Audit known data quality issues across source systems and pipelines; prioritize and resolve them in partnership with engineering and ETL ownership
• Stakeholder trust: Identify the 3-5 reports or metrics that leadership relies on most and make sure they are unambiguously correct before building new things on top of them

METRICS THAT MATTER HERE

You will be expected to understand, own, and improve these over time:

• Blended CAC and channel-level CAC across Meta, Google, affiliate, and organic.
• ROAS and contribution margin by paid channel, campaign, and creative.
• Full-funnel conversion rates from paid click through qualification, evaluation, and purchase.
• Email and SMS program health: deliverability, inbox placement, revenue per send, list growth vs. decay.
• LTV and cohort payback curves by acquisition channel and offer type.
• Primary-product-to-subscription conversion and subscription retention.
• Operational throughput and its downstream impact on conversion.
• Contact center efficiency: cost per handled contact, AI deflection rate, missed call rate.

YOUR TECH ENVIRONMENT

Category	Tools / Systems
Warehouse & BI	Microsoft Fabric  ·  Power BI  ·  SQL Server
E-commerce	Shopify  ·  subscription billing platform
Paid Media	Meta Ads  ·  Google Ads  ·  affiliate networks
Attribution	Multi-touch attribution platform  ·  GA4  ·  iOS ATT / probabilistic modeling
CRM & Email	Salesforce  ·  Salesforce Marketing Cloud  ·  dedicated IP infrastructure
Clinical & Ops	Salesforce operational layer
Contact Center	Cloud telephony  ·  AI voice assistant
AI & LLM	Claude Enterprise (Anthropic)  ·  custom MCP server  ·  skills / data dictionary layer  ·  open-source LLM exploration

QUALIFICATIONS

Required

• 5 to 8 years in data analytics, business intelligence, or marketing analytics, with hands-on ownership of a BI environment.
• Expert SQL. Comfortable writing complex analytical queries against large, imperfect datasets.
• Hands-on Power BI experience: dashboard design, semantic modeling, and enterprise deployment.
• Working knowledge of Python for data wrangling, automation, and lightweight analysis.
• Strong command of paid media analytics: Meta Ads, Google Ads, multi-touch attribution, ROAS, and CAC methodology across channels.
• DTC funnel fluency: conversion rate analysis, cohort work, LTV, and promo / offer performance.
• Executive presence. You can translate complex data into clear, decision-ready narratives and hold your own in a room with the CEO and Chairman.
• Hands-on use of Claude or comparable LLMs as part of your analytical workflow, not just awareness.

Preferred

• Direct experience with Microsoft Fabric or Azure Synapse Analytics.
• Experience with Salesforce Marketing Cloud as a data source (send / bounce logs, journey data) and Salesforce CRM objects.
• MCP server familiarity. Hands-on experience building or shaping skills / context layers for an LLM is a meaningful plus.
• Prior work in DTC health, wellness, or subscription commerce.
• Shopify as a data source (order schema, subscription billing artifacts, refund logic).
• Experience auditing and remediating data quality issues at scale, not just flagging them.
• Exposure to open-source LLM deployment or fine-tuning.

WHO THRIVES HERE

• You are a fixer, not a greenfield-only builder. You see a messy warehouse and feel motivated, not repelled.
• You move between strategic and tactical fluidly. You can present to the CEO at 9am and be debugging a DAX measure at 10am.
• You have strong opinions about metric definitions and you defend them with data, not politics.
• You proactively surface insights instead of waiting to be asked. If something looks wrong, you say so.
• You document your work. Clean data dictionaries and well-commented SQL are points of pride, not afterthoughts.
• You are energized by the pace of a growth-stage company where your work visibly changes decisions.