Data Analyst

Zero Homes•Denver, CO

7d•Hybrid

About The Position

Zero Homes is seeking a Data Analyst to establish the foundational data infrastructure for the company. This role will report to the Sr. Manager of Business Operations and will be responsible for transforming data from various sources (sales, marketing, fulfillment, supply chain, and finance) into reliable, queryable datasets and analyses. The Data Analyst will address the current challenge of fragmented data by modeling it properly, ensuring accessibility, and performing analyses to drive decision-making. The goal is to become the trusted source for critical numbers within the company.

Requirements

3-6 years in data analytics, analytics engineering, or a similar technical analytical role, preferably at a high-growth company.
Strong SQL skills, including daily use, data modeling capabilities beyond basic querying, and the ability to identify data inaccuracies.
Technical proficiency in Python or R for analysis.
Comfort with version control.
Ability to build durable solutions rather than one-off spreadsheets.
Business acumen to understand funnels, field operations, and customer experience, and to identify impactful questions.
Strong communication skills, including presenting findings to leadership, simplifying complex analyses, and making recommendations.
Intellectual honesty, reporting data accurately even when it contradicts popular narratives or prior analyses.
Experience using AI tools to enhance speed and efficiency in analytical tasks.

Nice To Haves

Experience in home services, energy, construction tech, marketplaces, or businesses with physical operations.
Experience building a data stack from an early stage (warehouse, transformation, BI).
Experience with PostHog or other product analytics tooling.
Experience with job costing, project-based economics, or external partner reporting.

Responsibilities

Build and own core datasets by modeling data from HubSpot, PostHog, the company platform, and financial/operational sources into clean, documented tables.
Ensure data quality end-to-end by instrumenting new processes for measurability and proactively identifying and fixing data issues.
Document metrics, definitions, sources, and methods to ensure consistent understanding and reproducibility of analyses.
Improve the efficiency of future data-related questions by enhancing the data model.
Perform analyses such as funnel conversion by market and channel, install throughput and cycle times, capacity utilization, cohort behavior, and unit economics.
Investigate the root cause of data changes, examining data, processes, and people.
Support the measurement of experiments and programs, collaborating with Growth and Sales teams.
Deliver clear, decision-ready findings with quantified impact.
Build reporting and tooling to enable self-service data access for recurring questions.
Develop durable tooling rather than one-off spreadsheets to ensure work compounds over time.
Assist teams in formulating better data-related questions and serve as a trusted resource.
Leverage AI tools to accelerate the process from question to query to answer.