Staff Analytics Engineer

Description

The company is an applied AI firm serving construction builders. Its AI agent workforce helps fast-growing contractors with permitting, licensing, inspections and project closeouts, supporting housing, clean-energy and infrastructure work across the US.

Construction is a $1.6T industry that still faces major delays, wasted capital and missed opportunities. The company has delivered faster, more accurate and more transparent permitting work on over $20B in development, helping contractors cut compliance time, lower project risk and grow with confidence.

The US is entering a new capex cycle across data centers, factories, housing and renewables, and the company is building the AI infrastructure behind construction projects in that wave of reindustrialization.

The company has raised over $90M, most recently in a Series B, from investors including Accel, Kleiner Perkins, Initialized, Y Combinator, Felicis and Altos Ventures. Backers include leaders from OpenAI, Google, Procore, ServiceTitan, Zillow, PlanGrid and Uber.

The role is a Staff Analytics Engineer who will build and shape the data foundation and intelligence layer for the company's next phase of growth.

This is a hands-on individual contributor role combining analytics engineering, data analysis and strategic decisions. You will own problems from start to finish: designing the data warehouse, building dbt models and pipelines, setting trusted metrics, investigating hard business questions and turning findings into insights that move the business.

You will work directly with leaders and teams in Engineering, Product, Operations and Customer Success. Instead of running a central reporting function, you will work alongside the business to find its biggest challenges, spot opportunities in the data and build the analysis needed to act on them.

The role calls for deep technical skill and sound business judgment. Most of your time will go to SQL and dbt, with hands-on Python work, data exploration, analytical modeling and presenting recommendations that shape how the company runs and grows. Good analytics engineering means making better decisions possible, not only building reliable models.

The company is building an AI-native data function. You will help rethink analytics workflows with LLMs and new technologies, from automating complex analysis to letting teams reach and act on data on their own.

You will have the technical ownership to shape the data architecture, the business access to influence company-wide decisions, and the freedom to build capabilities that grow in value as the company scales.

Responsibilities

• Architect, build and scale the data warehouse, dbt models, transformation pipelines and core metrics, creating well-structured data that the whole company can trust. dbt will be central to your daily work.
• Work with cross-functional leaders to find high-value opportunities, investigate unclear business problems, run deep-dive analyses and turn complex data into clear, actionable recommendations.
• Explore and implement LLM-powered workflows, AI agents and automation that speed up analysis, cut manual work, improve data access and change how teams use information.
• Set the standards for data modeling, architecture, testing, documentation, observability and governance, and build systems that keep data consistent and reliable as the business grows.
• Work with leaders in Operations, Customer Success, Product and Engineering to define KPIs, assess business performance, find operational bottlenecks and find ways to improve efficiency, growth and customer outcomes.
• Build dashboards, reporting systems and self-service tools so teams can answer complex questions on their own and decide faster.
• Help shape the long-term data architecture, tooling, technical roadmap and operating model, including how an AI-native data function should run as the company grows.

Requirements

• Significant experience in analytics engineering, data engineering, data analytics or business intelligence, including a record of owning complex, high-impact work independently.
• Expert SQL and deep hands-on experience building production-grade dbt models, designing dimensional data models and developing transformation workflows in modern cloud data warehouses.
• Solid engineering fundamentals in Python, with experience building reliable, scalable data pipelines, analytical workflows and data infrastructure.
• Practical experience using LLMs, AI agents or AI-powered tools to improve analytics, automate processes or make data easier to access. You are curious about new technology and willing to challenge standard approaches.
• Strong engineering skills alongside real analytical depth. You can design data models, write production-quality code, explore datasets and produce insights that guide business decisions.
• Sound analytical and business judgment. You can break down unclear problems, ask the right questions, spot meaningful patterns and turn findings into clear recommendations with measurable impact.
• A record of independently leading complex, cross-functional work from problem definition through delivery. You can set technical direction, influence architecture and raise engineering quality without formal authority.
• Clear communication and influence. You can work with engineers, executives and business operators, explain technical trade-offs, present findings and persuade through clear reasoning.
• A builder's mindset. You are proactive, resourceful and motivated by hard problems in a fast-moving setting, and you take ownership beyond your own remit.

Conditions

Hands-on, individual contributor role.

Employment decisions are based on merit, qualifications and business needs, and the company is committed to a diverse and inclusive workforce open to people of all backgrounds, experiences, perspectives and abilities.

Benefits

• A competitive salary and meaningful equity in a high-growth company.
• 100% company-paid base medical, dental and vision coverage for employees, plus a healthcare FSA.
• 401(k) savings plan.
• Unlimited PTO and paid family leave.
• Home office and equipment stipend.
• Daily in-office lunch and dinner provided.
• Commuter benefits covering pre-tax transit and parking.
• A venture-backed company still scaling, where you can shape foundational systems and the data function before it becomes fixed.
• Direct work with company leadership, with visibility into strategy and a say in decisions at every level.
• Trust and ownership to take initiatives from idea to production, set technical standards and see your work affect business outcomes.
• Help define the data capabilities, technical culture and operating model that support the company's next stage of growth.