Business Intelligence Analyst (Real Estate)

Career Certified

9d•$90,000 - $110,000•Remote

About The Position

The Business Intelligence Analyst sits on the FP&A team but serves the entire organization. You will query data from our data lake, turn it into clean and trustworthy reports and dashboards, and deliver insight that helps leaders across the business make better decisions. This is a hands-on, data-focused role built for someone early in their career who is fluent with modern tools, including AI, and eager to grow into a broader analytics or finance career. This is a high-visibility seat: your work will inform decisions made by department leaders, the executive team, and our board and investors. You will not be expected to know everything on day one, but you will be expected to be curious, precise, and quick to learn.

Requirements

Strong commitment to data integrity, careful, detail-oriented, and skeptical of your own numbers until they tie out to source.
Able to explain findings clearly to non-technical stakeholders (ELT and Board level) in writing and in conversation.
Digs into why a number looks off instead of taking it at face value, and proactively flags or fixes reporting gaps, inefficiencies, and stale processes rather than waiting to be asked.
Picks up new tools quickly without getting attached to a specific platform, juggles multiple stakeholder requests at once, and favors the simplest report that answers the question over the most complex one.
Hands-on experience building and maintaining recurring reports or dashboards in a modern business intelligence platform. We are tool-agnostic; transferable skills matter more than any specific product.
Working proficiency in SQL, including joins, aggregations, and filtering across multiple tables; comfortable querying a cloud data warehouse or data lake.
Strong Excel skills, including pivot tables, lookups, and clean, well-structured workbooks.
Practical experience using AI tools in a data or reporting context, and the ability to apply them to automate and improve analytics work.

Nice To Haves

Familiarity with Python or R for data manipulation.
Understanding of finance or FP&A concepts (budgets, actuals, variance, KPIs).
Experience with version control (Git) or dbt / data-transformation tooling.
Exposure to a B2C and/or B2B subscription or e-commerce business.
Data visualization or design sense, with an eye for building dashboards that are clean and readable, not just functional.
Direct experience working directly in a cloud data warehouse or data lake environment.

Responsibilities

Query our cloud data lake using SQL and AI-assisted tooling to answer business questions and produce the inputs behind recurring reporting.
Build and maintain recurring reports and dashboards, KPI trackers, operational metrics, and finance and business performance views, in the company's business intelligence platform.
Develop Excel-based models and reports, and help translate ad-hoc spreadsheet analysis into scalable, repeatable reporting.
Embed AI into our reporting processes, using tools such as Claude and Copilot to automate data preparation, accelerate analysis, and build AI-assisted outputs into how reports are produced and delivered.
Own data integrity across your reporting: reconcile figures to source systems, validate them against defined business rules, and proactively surface and resolve discrepancies before anything reaches a stakeholder.
Deliver business insight and data clarity, translating stakeholder questions across departments into well-scoped analysis, and presenting results in a way that makes the story in the data clear and actionable.
Establish and maintain documentation for data definitions, report logic, and reporting processes, ensuring consistency, transparency, and continuity across the analytics function.
Help build a scalable reporting and data infrastructure, standardizing datasets, automating manual steps, and extending the company's unified data layer so reporting can grow without adding headcount.
Partner with leaders across the business to understand their reporting needs and turn them into dashboards and reports they use.
Collaborate with data engineering on schema changes, new data sources, and pipeline issues that affect downstream reporting.