Lead Data Analyst

Job Description: Lead Data Analyst

Position Overview:

The Lead Data Analyst is a 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 executive team and leaders across the enterprise.

How We Work:

The company’s data function is intentionally lean. Our day-to-day environment is Google Cloud: we favor a small number of well-modeled, wide, analysis-ready tables in BigQuery, built and tested in Dataform, over a large catalog of hand-built reports. AI tools (such as Claude, ChatGPT, Cursor and GitHub Copilot) are part of daily work for writing and reviewing SQL and Python, documenting models and building analyses quickly, with the analyst fully accountable for accuracy. Success is measured by better decisions, not by the number of dashboards.

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.

Analytics Engineering:

·      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:

·      7 or more years of hands-on analysis 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.

·      Hands-on technical skills are a must: strong SQL and Python, and you do the analysis yourself rather than directing others to. With today’s AI tools, fluency and good judgment matter more than deep technical expertise.

·      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.

·      Demonstrated daily use of AI tools to write, debug and document code and to accelerate analysis, with the judgment to verify their output.

·      Experience building SQL transformation pipelines in Dataform or dbt preferred (day-to-day work is in Dataform); comfortable working in Git.

·      Experience with Google Cloud and BigQuery preferred; experience with another cloud warehouse such as Snowflake or Redshift is acceptable.

·      Working knowledge of applied machine learning (regression, classification, clustering, forecasting) and the judgment to know when a simple model beats a complex one. Deep modeling or ML engineering expertise is not required.

·      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.

·      Bachelor’s degree in a quantitative or business field, or equivalent experience.

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.

Expectations:

·      Little or no business travel is expected.

·      Adapt to evolving business needs and continuously raise the bar on how analytics is done with AI.