Note: The job is a remote job and is open to candidates in USA. Macabacus is a productivity and brand compliance solution for finance, banking, and consulting teams. The Senior Data Analyst will own the data function, building analytics foundations and reporting across go-to-market performance, product usage, customer adoption, and executive metrics. This hands-on leadership role will also establish data standards, guide business strategy, and promote AI-assisted workflows.
Responsibilities
- **The data function, end to end**. You are our data team. You'll set the strategy, establish standards for data quality and definitions, and build the foundation that scales as we grow. When a project exceeds what one person should build alone, like data pipeline infrastructure, you'll know it, scope it, and bring in the right contractors, vendors, or tools to get it done
- **GTM and funnel visibility**. Build the reporting backbone for our marketing and sales funnel: lead flow, conversion rates by stage, pipeline creation and velocity, campaign performance, and win/loss patterns. You'll work inside our GTM systems (Salesforce and our marketing automation platform), understand how the data is generated, and fix the data quality issues at the source, not just downstream
- **Product usage and adoption insight**. Stand up reporting on how customers actually use the product - activation, feature adoption, user engagement, and the signals that precede expansion or churn - and connect it to revenue data so we understand the full customer picture
- **Executive and board reporting**. Own the recurring metrics package for leadership and the board. That means numbers that reconcile, definitions that hold up under scrutiny, and narrative-quality presentation - clear takeaways, not chart dumps
- **Business partnership.** Build trusted relationships with marketing, sales, product, customer experience and finance leaders. Understand their goals well enough to anticipate what they need, push back when a request won't answer the real question, and proactively bring insights they didn't ask for
- **An AI-forward way of working**. As a team of one, your leverage matters. We expect you to default to AI-assisted approaches where they make you faster or better in your own workflow (analysis, SQL, documentation, QA) and in how you evaluate tooling for the data stack. You'll also help shape how the broader company uses AI to work with data, including making insights more self-serve so you're not the bottleneck for every question
Skills
- 8+ years in data/analytics, with at least 2 years as the first, only, or lead data person at a startup or scale-up — ideally B2B SaaS in the $5M–$50M ARR range. You've built a data function from little or nothing before
- Expert SQL and strong data modeling instincts. You're comfortable in large, messy, imperfect datasets and know how to make them trustworthy
- Deep GTM systems fluency. Hands-on experience with Salesforce data and reporting, marketing automation platforms (HubSpot, Marketo, or similar), and product analytics tools (Amplitude, Mixpanel, Pendo, or similar). You understand how a lead becomes revenue and where the data breaks along the way
- Strong BI craftsmanship. Advanced Tableau (or equivalent, with willingness to work in Tableau) and a track record of dashboards that executives actually use
- Working knowledge of the modern data stack: cloud warehouses (Snowflake, BigQuery, Redshift), ELT tools (Fivetran or similar), and dbt or comparable transformation tooling. You can stand up and administer a lean stack yourself, and you know when a problem calls for a specialist instead
- Executive-grade communication. You've presented to executives and boards, and your work is polished enough to go in front of them without rework. You can translate analysis into 'here's what this means and what we should do.'
- AI fluency as a working habit, not a buzzword. You actively use AI tools (Claude, ChatGPT, Copilot, or similar) in your daily analytics work and can point to concrete examples of where they made you meaningfully faster or better and where they fell short and you didn't use them. You stay current on AI capabilities in the data/BI space and factor them into build-vs-buy decisions
- Structured, reusable AI workflows. You've moved beyond one-off prompting: you build reusable context (CLAUDE.md / AGENTS.md or similar), run analysis through agentic tools like Claude Code or Codex, and turn recurring analyses into automated, centralized workflows rather than manual tasks that pile up
- Builder's judgment. A track record of pragmatic build-vs-buy-vs-outsource decisions, and comfort operating with ambiguity, limited resources, and competing priorities
- Industry experience in finance or fintech SaaS
- Python for analysis or automation
- Experience with revenue operations or GTM operations
- Experience defining SaaS metrics (ARR movements, NRR, CAC, LTV, funnel conversion) alongside finance
- Experience scoping and managing data engineering contractors or agencies
Company Overview