Senior Data Analyst - Business Insights & Operations

Spinwheel is building infrastructure for intelligent debt management and financial empowerment through real-time credit data, optimization engines, AI-driven workflows, and financial APIs. The Senior Data Analyst will build the company’s shared data foundation, including unified pipelines, dashboards, metric definitions, and automated reporting, while partnering cross-functionally to improve revenue, product usage, customer health, and executive visibility. The role will also develop AI-enabled agents and automation for self-service analysis, anomaly detection, and business reporting.

Responsibilities:

• Design and maintain unified reporting that pulls from multiple systems (CRM, Accounting, Invoicing, Operational DBs, etc.) for one consistent, trusted view
• Replace ad hoc, manually-synced exports with reliable, more real-time data pipelines and refresh cadences
• Define and document shared metric definitions (e.g., MRR, activation, churn) so every function reports the same numbers the same way
• Work closely with Data Engineering on the architecture, reliability, and scalability of the pipelines feeding Redshift and MongoDB Atlas, flagging gaps that affect reporting quality or freshness
• Serve as a key voice in evaluating and selecting BI and analytics tooling, including assessing whether our current tool, ThoughtSpot, still meets the business's needs as we scale, and helping lead a transition if a change is warranted
• Represent the reporting and analytics perspective in broader data stack decisions, including warehouse, ELT/ETL, data catalog, and governance, so tooling choices serve the people using the data, not just the people moving it
• Partner with Finance to extend revenue reporting (MRR/ARR bridge, new vs. expansion vs. churned revenue, revenue by segment and product) into the shared dashboards other functions rely on
• Help surface collections and billing risk (AR aging, failed payments) and support reconciliation between bookings, invoiced, and collected revenue, working alongside Finance
• Build cohort and retention analyses (gross and net revenue retention) that build on Finance and RevOps reporting to explain the drivers behind revenue movement for the rest of the business
• Query MongoDB Atlas (our platform's operational database) directly, using the aggregation framework, to build adoption, activation, and engagement dashboards so Product and Leadership can see how the platform is actually being used
• Track API and feature usage by customer and endpoint, and connect usage patterns to retention and expansion
• Measure the impact of new releases and features on usage and adoption metrics
• Build a composite customer health score combining usage, support activity, and sentiment
• Flag renewal risk and expansion opportunity for Customer Success and Sales before it becomes a surprise
• Bring rigor to customer usage reporting so it's a reliable input to CS and GTM decisions, not an afterthought
• Partner with Revenue Operations (not duplicate it) to connect pipeline, funnel, and campaign data to the rest of the business's metrics
• Support win/loss, attribution, and sales-cycle analysis where it intersects with revenue and product usage
• Own the recurring business review package that gives Leadership one place to see the whole business
• Support board reporting and OKR/goal tracking with consistent, defensible numbers
• Be a thought partner to every function leader, translating their questions into analysis rather than just tickets
• Build a Claude-powered natural-language query agent that turns a plain-English question ("which customers' API usage dropped last month?") into a MongoDB aggregation pipeline or a Redshift SQL query and returns a vetted answer, so Product, CS, and Leadership can self-serve instead of filing a request with Data
• Build automated anomaly detection and alerting that continuously watches key metrics (MRR, API call volume, activation, churn signals) and proactively posts to Slack when something looks off
• Use AI to draft a first-pass "what happened and why" written summary for each business review, which you then review and sharpen
• Use AI to spot and reconcile mismatches across systems, such as the same customer represented differently in HubSpot, QuickBooks, and MongoDB Atlas
• Use AI to mine call transcripts (Grain and Granola) and support tickets for recurring themes, objections, and risk signals, and fold that into the customer health score
• Package these agents and workflows as reusable internal tools so other functions can extend or reuse them, rather than treating every new question as a one-off analysis request
• Map every system of record (HubSpot, QuickBooks, MongoDB Atlas, Jira, Freshdesk, etc.), how they connect today, and where the gaps are
• Identify the top three to five highest-value reporting gaps (revenue, product usage, customer health) and have a plan to close them
• Product usage and customer health reporting are trusted and used regularly by Product and CS leadership
• Revenue reporting is timely, reconciled, and no longer dependent on manual, infrequent exports
• At least one AI-driven reporting agent or automated workflow is live and adopted by a function outside of Data

Requirements:

• 5+ years in data or business analytics, analytics engineering, or a similar cross-functional analytics role
• Strong SQL and hands-on experience with a cloud data warehouse (ideally Redshift) and a BI/visualization tool (we currently use ThoughtSpot; experience with Looker, Tableau, Mode, Hex, or Power BI also welcome)
• Demonstrated use of AI tools (Claude, ChatGPT, or similar) to automate analysis, build agents, or accelerate reporting workflows
• Experience building data models or a semantic layer that multiple teams can report from consistently
• Comfort connecting and reconciling data across disparate systems (CRM, billing/ERP, product analytics, support/ticketing platforms like Freshdesk)
• Excellent stakeholder skills, able to work equally well with Engineering, Data Engineering, Finance, Sales, CS, and Leadership, and to push back productively when a request needs sharpening
• Hands-on experience querying NoSQL/document databases, ideally MongoDB and its aggregation framework, a plus given our platform's operational data lives in MongoDB Atlas
• Experience in fintech, SaaS, or a usage/subscription-based business model
• Comfortable using AI coding tools (e.g., Claude Code) to build and maintain automation pipelines, with enough Python/SQL literacy to review, debug, and validate the code they generate
• Experience partnering with (not replacing) existing Finance and Revenue Operations analytics functions
• Experience evaluating, selecting, or migrating BI/analytics tooling (e.g., ThoughtSpot, Looker, Tableau, Mode, Hex, Power BI) as a business scales