Lead Data Analyst

Our client is partnering with Korn Ferry on their search for a Lead Data Analyst.

Our client is a top rated residential real estate brokerage in the US, helping families buy and sell their homes. They are the #1 real estate team in the industry for client satisfaction, with over 12,000 5-star reviews. The brokerage serves thousands of families each year and closes billions of dollars of real estate transactions annually by offering homeowners several home selling programs, including getting the best possible Guaranteed Offer for qualified clients.

Our client is poised to more than double the volume of homes sold annually by leveraging technology to better serve clients and pursuing geographical expansion to serve even more families.

The Lead Data Analyst is the senior, hands-on owner of business analytics. This is an independent, high-impact individual contributor role for someone who is as comfortable in a room with sales, marketing, operations and finance leaders as they are in SQL, Python and Dataform. The Lead Data Analyst works directly with business leaders to understand their problems, frame the right questions, find the answer in the data and help drive the decision, then builds the data models and lightweight data products that make that answer repeatable.

The role uses modern, AI-assisted tooling to do work that historically required separate BI developers and analytics engineers. The position is based at the company’s headquarters in Alpharetta, GA, reports to the SVP of Product, Data & Applied AI, and partners closely with the executive team and leaders across the enterprise.

• Serve as the go-to analytics partner for the executive team and leaders across the enterprise in sales, marketing, operations, Guaranteed Offer and finance, spending meaningful time with the business to understand how it works and where value is created and lost.
• Turn loosely defined business questions into well-framed problems, agree up front on the decision the analysis will inform, and deliver a clear recommendation rather than just a chart.
• Proactively identify opportunities, risks and inefficiencies across the client funnel (lead, appointment, listing, contract, close), agent productivity, pricing and marketing spend and bring them to leadership before being asked.
• Prepare and present findings to the executive team and enterprise leaders in plain business language, with clear implications and next steps.
• Measure the results of decisions and initiatives after the fact so the business learns what worked and what did not.

• Perform deep-dive analysis in SQL and Python, including funnel and conversion analysis, cohort and retention analysis, segmentation, forecasting, unit economics, marketing attribution and root-cause investigations.
• Apply sound statistical reasoning (test design, pre/post and A/B comparisons, regression, adjusting for mix, seasonality and cohort maturity) and know when a result is noise.
• Apply practical machine learning where it improves decisions, such as lead scoring, conversion and propensity models, forecasting, and pricing or time-on-market estimates, using tools like Big Query ML, Vertex AI or scikit-learn; validate models honestly, explain them in plain business terms, and put useful ones into everyday workflows.
• Own consistent measurement and definitions of core KPIs so that all leaders rely on the same set of facts and one source of truth.
• Produce the monthly and quarterly business review metrics, supply the analysis behind the SVP’s board reporting, and support FP&A with driver-based inputs to budgeting and forecasting.
• Own the transformation layer in Big Query using Dataform:…