Analytics Engineer / Operations Intelligence Lead

Collective is on a mission to redefine the way businesses-of-one work. Our technology and team of trusted advisors help members achieve financial independence by taking care of everything from business incorporation to accounting, bookkeeping, tax services, and access to a thriving community, all in one integrated platform. We believe in empowering self-employed people to enjoy the same tax savings that big companies get, so they can focus on their passion, not paperwork.

About the role:

You will own the architecture and buildout of Collective's Operations Intelligence Warehouse: the BigQuery data layer and Metabase BI that make throughput, quality, variance, and capacity inspectable across an AI-native financial operation. You will also own the analytics on top of it: spotting trends before they become problems, surfacing the signal leadership acts on, and seeing around corners with evidence rather than instinct. The AI in an AI-native financial services firm is only as good as the operation it learns from, and the operation is only as good as the data that describes it. That data layer is yours.

You will join Strategic Operations, the applied operations research team that designs how Collective's member delivery engine runs. Reporting to the Head of Strategic Operations, you will lead the design of our operational data, set the modeling and quality standards the team builds on, and turn raw production-line telemetry into the operating metrics and insights the business runs on: cycle time, first-pass yield, utilization, and cost-to-serve.

What you'll do:

• Turn operational questions into actionable answers. Take questions like "are we under capacity in tax review" or "why did first-pass yield drop in bookkeeping" and turn them into measurable definitions, the right data, and a defensible answer.

• Instrument the operation. Design and own the Metabase dashboards and metric definitions that give leadership real-time visibility into operational health: throughput per workflow, cycle time, first-pass yield, exception rates, and capacity versus demand.

• Connect operations to revenue. Join operational data to GTM and financial data to understand: how service quality drives retention and NRR, what each workflow costs to run, and margin along the delivery pipeline.

• Power statistical rigor. Lead variance analysis, statistical process control, and root cause analytics that replace anecdote with evidence in daily operating decisions.

• Architect the Operations Intelligence Warehouse. Your first few months center here: audit, clean up, and re-architect the Operations, GTM, and financial subset of our BigQuery warehouse, setting source-of-truth definitions, schema design, and naming standards. You own it ongoing.

• Raise the data practice. Build and maintain dimensional models in dbt with testing, lineage, and version control. Partner with Data Engineering, who own the company-wide pipelines and product schemas, to establish the SQL, modeling, and analytics engineering standards Strategic Operations scales on.

What you'll bring:

• Experience: 5+ years in data analysis or analytics engineering at a B2B SaaS or fintech company, ideally one with a human-in-the-loop service delivery operation.

• Analytical Powerhouse: Expert-level SQL and hands-on experience with dbt (data build tool), or equivalent, in a modern warehouse, BigQuery and Metabase strongly preferred.

• Warehouse Wrangler: A track record of leading a data cleanup or warehouse re-architecture: you have untangled inconsistent schemas, established sources of truth, and made a messy warehouse trustworthy.

• Quality-First: Strong data quality instincts: testing frameworks, lineage, documentation, and git-based workflows are how you work, not an afterthought.

• Signal from Ambiguity: Proven ability to take ambiguous operational questions and turn them into structured, measurable analyses that change decisions.

• Cross-Functional Influence: Exceptional communication and stakeholder management skills, with a track record of effectively collaborating between highly technical teams (Product/Eng) and specialized subject matter experts (Tax/Accounting).

• Startup Agility: A self-starter mindset with the ability to thrive in a fast-paced environment, pivot when necessary, and comfortably navigate a high-growth startup ecosystem.

Nice to have:

• Python for analysis and pipeline work.

• Exposure to statistical process control, six sigma, or industrial engineering methods.

• Experience instrumenting AI agent or workflow telemetry, or supporting LLM eval programs.

• AI-assisted, low-code development experience (Claude Code, AppSheet, or similar).