Senior BI Analyst

RealWork•Austin, TX

10h

About The Position

As a Senior BI Analyst at RealWork, you’ll be the analytical brain behind how our product gets better, how our customers get more value, and how the business makes smarter decisions. You’ll sit at the intersection of Product, Engineering, Customer Success, and the executive team, and your work will directly shape what we build, how we measure it, and how we know it’s working. This is not a dashboards-on-demand role. What we need is someone who can look at the data, form a real point of view about what’s going on in the business, and then go build the analysis that proves or disproves it. You’ll be expected to ask better questions than the people asking you for reports, and to translate raw data into recommendations a CEO or product lead can act on tomorrow. This is also an AI-native role. We expect you to use modern AI tools as a core part of how you work every day: writing and debugging SQL, exploring datasets, drafting analyses, building dashboards, and pressure-testing your own thinking. If your current workflow doesn’t already include AI as a daily collaborator, this isn’t the right role for you.

Requirements

4–6 years in a product, business, or data analytics role, ideally at a SaaS company. Comfortable owning analysis end-to-end, not just executing on someone else’s spec.
Strategic thinker first, technician second. You can look at a business problem, frame the right questions, and design the analysis that answers them. You’re not waiting to be told what to look at.
Strong opinions about what makes a good metric and a bad one. You can spot a vanity number, a misleading chart, or a dashboard nobody uses, and you have the judgment to push back.
An AI-native analyst. You use AI every day to write SQL, explore data, draft analyses, build visualizations, and sharpen your thinking. You can describe specific ways AI has changed how you work in the last twelve months.
Expert-level SQL. You can write, read, and debug complex queries in a cloud data warehouse without hand-holding. BigQuery experience is a plus.
You are familiar with modern BI tooling (Power BI, Looker, Tableau, or similar acceptable) but are excited about exploring AI-native analytical and dashboarding solutions.
Comfortable in data-rich environments; moving data around, working across warehouses and operational systems, and standing up the pipelines you need rather than waiting for someone else to build them.
Experience with product analytics tooling (Pendo, Amplitude, Mixpanel, or similar) and a working understanding of event modeling, customer journey funnels, and time-series metrics.
Strong written and verbal communication. You can take a messy analysis and turn it into a one-page recommendation a non-technical executive can act on.
Curious by default. You ask the next question, then the one after that, until you actually understand what’s going on.

Nice To Haves

Have web analytics experience (GA4, GTM, server-side tracking) and understand how to measure marketing and conversion performance, not just product usage.
Have hands-on experience designing and analyzing A/B tests, including the statistics behind sample sizing and significance.
Have written production-grade Python or used dbt to manage transformations and models.
Have a real point of view on where the analytics discipline is going in an AI-native world, and want to help us get there first.

Responsibilities

Own product and customer analytics end-to-end; from the event model and data tables to the dashboards executives, product leaders, and CS managers actually use.
Partner with Product and Engineering to instrument new features, define what success looks like before launch, and report back on whether we hit it.
Dig into customer behavior to identify retention drivers, churn signals, and the engagement patterns that separate our best customers from the rest and translate those findings into product and CS recommendations.
Build and maintain a set of executive and operational dashboards that tell the story of the business at a glance. Kill the ones that don’t.
Design and analyze A/B tests for product changes, onboarding flows, and CS interventions, with clear hypotheses, sample sizes, and readouts.
Use AI tools every day to move faster - generating SQL, exploring data, drafting analysis, and producing visualizations - and help us raise the bar for what an AI-native analytics function looks like.
Work cross-functionally with RevOps, BI, Engineering, Product, and CS to make sure the data we trust is the same data everyone else sees.
Bring strategic thinking to the table. We don’t want a ticket-taker; we want someone who will tell us what we should be looking at next.