Lead Data Analyst

Korn Ferry; Alpharetta (GA); Presencial; USD 110.000 - 160.000; Jornada completa

Descripción de la vacante

Korn Ferry is assisting in recruiting a Lead Data Analyst for our client, a top residential real estate brokerage, based in Alpharetta, GA. This senior IC role partners with sales, marketing, operations and finance to frame problems, build models and drive decisions using SQL, Python and modern AI-enabled tooling.

The candidate will own KPI definitions, dashboards and data products, leveraging Dataform, BigQuery and Looker to deliver high-impact insights for executives and boards.

Formación

• Bachelor’s degree or equivalent experience in a quantitative field.
• 7+ years of hands-on analytics with business impact.
• Expert SQL and strong Python for analysis.

Responsabilidades

• Serve as the analytics partner to executives across functions.
• Translate business questions into framed problems and actionable recommendations.
• Develop data models and lightweight data products for repeatable insights.
• Own the transformation layer in BigQuery using Dataform; maintain tested models.
• Deliver insights via memo, HTML reports, notebooks or dashboards.

Conocimientos

SQL

Python

Data analysis

Data modeling

BigQuery

Dataform

Educación

Bachelor’s degree in Statistics, Economics, Mathematics, CS, or related field

Herramientas

Git

Looker

dbt

BigQuery

Descripción del empleo

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.

Position Overview:

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.

Key Responsibilities:

Business Partnership and Decision Support:

• 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.

Analysis and Insights:

• 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 BigQuery 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 BigQuery using Dataform: design and maintain clean, documented, tested, wide analysis-ready models (for example, one row per lead, listing, transaction or agent) that make most business questions a simple query.
• Use AI coding assistants to accelerate model development, testing, documentation and refactoring, while reviewing and validating all AI-generated output.
• Apply version control (Git), code review and automated Dataform assertions so metrics are reproducible and trustworthy.
• Maintain the metric definitions and semantic layer that dashboards, analyses and AI tools draw from.
• Rationalize and retire legacy reports, queries and duplicative tables as modern models replace them.
• Keep the Dataform project documented, tested and simple enough that the SVP of Product, Data & Applied AI and future engineering resources can support it, so the function never depends on one person.

Insight Delivery:

• Deliver insights in the lightest-weight format that fits the need: a written memo, an HTML report or interactive page, a notebook, a spreadsheet, or a focused dashboard in Looker or a comparable tool.
• Build and maintain a small set of high-value executive and operational dashboards rather than a large catalog of reports and favor self-serve access to well-modeled data.
• Automate recurring reports and analyses so they run without manual effort.
• Pilot AI-enabled analytics, such as natural-language questions over governed data, where it improves speed and access for business users.

Data Quality and Governance:

• Monitor data quality across source systems (Salesforce, marketing platforms, transaction and finance systems), surface issues and drive fixes with system owners.
• Maintain practical, lightweight documentation: metric definitions, model documentation, a data dictionary and known issues.
• Ensure appropriate access controls and handling of sensitive client and financial data.

Prioritization and Planning:

• Maintain a single, visible backlog of analytics requests, assess effort and expected impact, and communicate priorities and timing to stakeholders regularly.
• Partner with the SVP of Product, Data & Applied AI on priorities, data tooling, vendor and budget decisions, and keep data platform costs efficient.
• As the team grows, direct the work of data engineering resources supporting ingestion, integrations and infrastructure.

Qualifications:

• Bachelor’s degree in Statistics, Economics, Mathematics, Computer Science, Business Analytics, Finance, Engineering or a related field, or equivalent experience; advanced degree a plus but not required.
• 7 or more years of hands-on analysis and analytics engineering experience working directly with business leaders (for example in sales, marketing, operations or finance), with a track record of quickly getting up to speed on how a business works, framing its problems, and delivering analyses that changed decisions.
• Expert SQL and strong Python for analysis (pandas, notebooks, basic visualization).
• Working knowledge of applied machine learning (regression, classification, clustering, forecasting) and the judgment to know when a simple model beats a complex one; experience with BigQuery ML or Vertex AI is a plus. Deep modeling or ML engineering expertise is not required.
• Experience with Google Cloud and BigQuery strongly preferred; experience with another cloud warehouse such as Snowflake or Redshift is acceptable.
• Hands‑on experience building and maintaining SQL transformation pipelines in Dataform or dbt (either is fine; day‑to‑day work is in Dataform), comfortable working in Git.
• Demonstrated daily use of AI tools to write, debug and document code and to accelerate analysis, with the judgment to verify their output.
• Strong business acumen: understands P&L drivers, unit economics, sales funnels and operational levers, and can hold their own with senior leaders.
• Excellent communication skills with business audiences: can run a working session with leaders, write a crisp one-page memo, and explain complex findings simply and persuasively.
• Works quickly and pragmatically: delivers a useful directional answer in days rather than a perfect one in weeks, then iterates.
• Highly organized and self‑directed; able to manage multiple stakeholders and a backlog without close supervision.
• Experience directing the work of engineers or contractors, onshore or offshore, is a plus.
• Familiarity with Looker or another BI tool is helpful; deep report‑building or BI administration expertise is not required.
• Experience in real estate, lead generation, marketplaces or other high‑volume sales and service businesses preferred.

What This Role Is Not:

• Not a people-manager role. The Lead Data Analyst has no direct reports.
• Not a report factory. Dashboards are one delivery tool among several, not the primary output.
• Not an infrastructure engineering role. The focus is the analytics and Dataform modeling layer; deep cloud infrastructure and integration engineering are not the core of the job.

What Success Looks Like in the First Year:

• Core business KPIs are defined once, modeled in Dataform, and trusted by the executive team and enterprise leaders.
• A small set of wide, documented, tested models answers most recurring business questions.
• Legacy reports are rationalized and recurring reporting is automated.
• Several high-impact analyses have measurably changed decisions in sales, marketing or operations.
• A clear, prioritized analytics backlog that stakeholders understand and trust.
• Little or no business travel is expected.
• Adapt to evolving business needs and continuously raise the bar on how analytics is done with AI.