Über diese Data Analyst Stelle bei Zerohomes
About Zero Homes
Our mission is to rebuild American communities through healthy, affordable homes.
At Zero Homes, we operate on a simple premise: when you make contractors successful, homeowners win. The current home services industry is fragmented, complex, and opaque, which is frustrating for homeowners and exhausting for contractors. We are changing this by building a home upgrade marketplace that enables a simple, central, transparent process for homeowners and contractors to complete projects.
Through our platform we handle design, quoting, procurement, and customer service, allowing our contractor network to focus on what they do best and make them money: delivering projects. For homeowners, this delivers a predictable, high-quality, and transparent experience, transforming home upgrades from a source of anxiety into a seamless experience. Our vision is to create a world where homeowners and contractors in all 50 states can leverage our platform to execute thousands of projects every week within the next 36 months.
We work by a clear set of values:
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Be a "can kid," not a "can't kid": We own our outcomes and go to extraordinary lengths to solve problems.
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Go fast, be clear: Speed is our advantage, and we sweat the details to deliver an extraordinary customer experience.
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Customer & Contractor Obsessed: We have a deep, personal understanding of the work to be done. We stay curious about our contractors' craft and the stories of our homeowners.
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Win, as a team: We are building an iconic business that will reinvent the industry, and will do so together.
If you are ready to build a world-class consumer experience that genuinely rebuilds American communities, come build with us.
About the Role & Your Impact
We're looking for a Data Analyst to build the foundation the rest of the company thinks on. Reporting to the Sr. Manager of Business Operations, you'll turn the data flowing through every part of Zero (sales, marketing, fulfillment, supply chain, and finance) into trusted, queryable, reusable datasets and the analysis that runs on top of them.
Today our data lives in a lot of places: HubSpot, PostHog, our platform, spreadsheets, and program partner files. That means good questions take too long to answer and the same number sometimes comes back three different ways. Your job is to fix that at the root, then use it: model the data properly, make it accessible, and do the analysis that turns it into decisions. You'll be the person the team trusts when the number matters.
What You'll Do
Data Foundations
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Build and own our core datasets: model data across HubSpot, PostHog, our platform, and financial and operational sources into clean, documented tables everyone can rely on.
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Own data quality end to end: instrument new processes so they're measurable from day one, and find and fix the breaks before someone else finds them in a board deck.
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Document metrics, definitions, sources, and methods so numbers mean the same thing to everyone and analyses are reproducible.
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Reduce the cost of every future question by leaving the data model better than you found it.
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Do the analysis, not just the plumbing: funnel conversion by market and channel, install throughput and cycle times, capacity utilization, cohort behavior, and unit economics.
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Get to root cause. When a number moves, you dig through the layers (data, process, and people) until you understand and communicate why.
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Support experiment and program measurement, partnering with Growth and Sales on what actually moved the outcome.
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Deliver clear, decision-ready findings with quantified impact, not just charts.
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Make the business self-serve: build the reporting and tooling that answers recurring questions without a human in the loop.
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Build durable tooling rather than one-off spreadsheets, so the work compounds instead of expiring.
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Help teams ask better questions of the data, and be the person they trust when the number matters.
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Leverage AI tools to compress the time between question, query, and answer.
Analysis
Enablement & Tooling
What You Bring
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3-6 years in data analytics, analytics engineering, or a similarly technical analytical role, ideally at a high-growth company.
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Strong SQL. You write it daily, you can model data (not just query it), and you know why a number is wrong before someone tells you it is.
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Real technical range: Python or R for analysis, comfort with version control, and the ability to build something durable rather than a one-off spreadsheet.
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Business sense beyond the numbers: you understand how funnels, field operations, and customer experience actually work, and you pick the questions worth answering.
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Strong communication skills: you can present findings to leadership, make a complex analysis feel simple, and land a recommendation.
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Intellectual honesty: you report what the numbers say, including when they contradict the popular narrative or your own prior analysis.
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You've AI-enabled yourself to move fast across whatever stack you land in.
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Experience in home services, energy, construction tech, marketplaces, or businesses with physical operations.
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Experience building a data stack from an early stage (warehouse, transformation, BI) rather than inheriting one.
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Experience with PostHog or other product analytics tooling.
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Experience with job costing, project-based economics, or external partner reporting.
Nice to Have
