Senior Analytics Engineer

About the role

This position sits inside a Series C proptech company that builds AI growth tools for real estate entrepreneurs and you will be responsible for strengthening the platform that serves a large network of real estate businesses. Your work will ensure that decisions across the organization align with usage behavior and that the platform remains reliable and insightful for its users. You will operate at the intersection of data modeling, analytics, and product insight, translating complex platform interactions into clear, actionable information. The role requires a strong sense of ownership over the data stack, from pipeline logic to dashboard accuracy, to support a product recognized on BuiltIn Best Place to Work lists. You will work closely with real estate focused business teams to turn raw events into structured information that drives strategic choices. Nearly every modern company runs on data teams, from startups to large enterprises, and this role is central to that function within the real estate sector. A strong portfolio of past analyses and clear communication of results matter more than degrees in many hiring decisions for this kind of position.

Key facts

What you'll do

Ownership of data models ensures accuracy and relevance for platform analytics used by real estate businesses on a daily basis.

You will investigate experiment insights to shape the product suite, which includes award-winning web design, agile SEO solutions, and AI tools that serve entrepreneurs.

Performance dashboards will be designed and maintained to serve a broad audience of real estate professionals, communicating results that reflect platform impact and usage patterns.

You will build and monitor reliable data pipelines that power analytics workflows and support a platform recognized on BuiltIn Best Place to Work lists.

Collaboration with cross-functional partners will translate business questions into data specifications that guide analysis and metric design.

You will define and document metric definitions to align teams on what success looks like across the real estate platform and its AI tools.

Implementation of data modeling best practices in modern platforms such as Snowflake or BigQuery will improve consistency, speed, and trust in analytics outputs.

You will partner with product and growth teams to design tests, analyze results, and iterate based on evidence rather than intuition.

Communication of findings to non-technical stakeholders will require clear storytelling, concise visualizations, and practical recommendations.

Continuous improvement of dashboards, queries, and pipelines will ensure that analytics remain performant as data volume and complexity grow.

Requirements

United States work authorization and eligibility to work in the United States are required for this role and must be confirmed during the application process.

You must have experience owning data models in a production environment and demonstrating accuracy in platform analytics for real estate or similar sectors.

Strong proficiency in SQL is required, along with the ability to write complex queries that support experimentation and reporting needs.

Experience with modern data platforms such as Snowflake or BigQuery is necessary to manage scalable data storage and transformation.

You should be comfortable using programming languages such as Python to build analytics solutions and automate routine data tasks.

Demonstrated ability to design and build performance dashboards in tools like Tableau or Looker is required to serve a broad audience of real estate professionals.

A solid understanding of statistics and experimentation frameworks is required to interpret results and support product decisions.

Reliable communication skills are required to explain technical concepts to non-technical stakeholders and to document analytical decisions clearly.

Nice to have

Previous experience in the real estate sector or proptech environments where data drives product decisions is preferred.

Familiarity with agile development processes and collaboration with product and engineering teams in a fast paced setting.

Experience with data visualization best practices and storytelling techniques that help non-technical audiences understand complex insights.

Knowledge of data modeling techniques specific to event driven architectures and behavioral data.

Exposure to machine learning concepts or tools that support product teams in testing and optimization.

Practical notes

The position is full-time, and compensation ranges as stated on the apply page.

Employment eligibility requires United States work authorization.

The role is based in the United States, and remote arrangements may vary depending on team and policy.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study.

Candidates may be asked to design a metric, interpret an experiment, or build a small model as part of the technical assessment.

Some companies give a take-home analysis to evaluate practical skills and thought process.

Expect questions about past projects and the business impact of your work during behavioral interviews.

Interviewers often evaluate how you communicate uncertainty and business impact, not only the math, so clarity is essential.

Bringing a clean write-up of a past analysis to the interview is well received and can highlight your ability to structure and present findings.

Good to know

Analytics engineering commonly relies on SQL, Python, and modern data platforms such as Snowflake or BigQuery in this type of role.

Professionals in this role build dashboards with tools like Tableau or Looker and collaborate closely with product and growth teams in a real estate context.

The work involves experimentation frameworks, metric definitions, and data modeling for B2C SaaS products in the real estate sector, which can be complex and fast moving.

Questions to ask

Useful questions for the interview include what a typical week looks like, how work is assigned, what tools the team uses, and how feedback is given and incorporated.

Asking how the role has changed recently and what the team wishes it had known when joining can provide insight into growth and expectations.

Questions about the manager's priorities and how success is measured in the first months are especially valued by the team.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead as responsibilities and scope expand over time.

Many professionals specialize in areas such as machine learning, analytics, or infrastructure depending on interest and strength.

Cross-functional work with product and engineering teams becomes more important at senior levels as you influence roadmap decisions.

The field changes quickly, so continuous learning is part of the job and staying current with tools and methods is essential.

Professionals who can translate numbers into decisions and clearly communicate implications tend to advance faster and take on higher impact work.