About Collective: Financial solutions for self-employed business owners — formation, tax, accounting, and bookkeeping

About Collective:

Collective is on a mission to redefine the way businesses-of-one work. Our technology and team of trusted advisors help members achieve financial independence by taking care of everything from business incorporation to accounting, bookkeeping, tax services, and access to a thriving community, all in one integrated platform. We believe in empowering self-employed people to enjoy the same tax savings that big companies get, so they can focus on their passion, not paperwork.

Featured in Forbes, Business Insider, Yahoo, Bloomberg, Financial Times, TechCrunch, and more. We are backed by General Catalyst, Sound Ventures, QED Investors, Google’s Gradient Ventures, Expa, and other investors who have financed iconic companies like YouTube, Substack, Twitch, Box, Hims, Instacart, and Lyft.

About the role:

We are looking for a Data Engineer to own and scale the data platform that powers analytics, reporting, and AI across Collective. You will design, build, and maintain the pipelines that move data from our product, financial, and third-party systems into our BigQuery warehouse; model that data into clean, well-documented, reliable tables; and set the engineering standards that keep the platform trustworthy as the company grows.

You will join the Data Engineering team within Engineering and work closely with product engineers, analysts, and business stakeholders across Operations, Finance, and Go-to-Market. This is a hands-on role for someone who cares about data quality, takes ownership of production systems end-to-end, and wants their work to be the foundation the rest of the company builds on.

What you'll do:

- Design and build data pipelines. Develop, deploy, and maintain scalable batch and event-driven pipelines that ingest data from application databases, SaaS tools, and external APIs into BigQuery using managed connectors (Fivetran), custom Python loaders, and orchestration tooling.

- Model the data. Design and implement dimensional and analytical data models in dbt, following a layered architecture (raw, staging, marts) with clear grain, naming conventions, and documentation that analysts and downstream tools can rely on.

- Own data quality and reliability. Implement testing, monitoring, alerting, and data contracts across the pipeline; define and meet freshness and accuracy SLAs; triage and resolve pipeline failures and data incidents to root cause.

- Optimize performance and cost. Tune warehouse queries, partitioning, and clustering; manage BigQuery spend; and keep pipelines efficient as data volume grows.

- Establish engineering standards. Drive best practices for version control, code review, CI/CD, and infrastructure-as-code across the data stack; document systems and runbooks so the platform is maintainable by the team.

- Govern and secure data. Implement access controls, PII handling, and data retention practices appropriate for a financial services company; partner with Security and Legal on compliance requirements.

- Enable the business. Partner with product engineers on source schema design and change management, and with analysts and stakeholders to translate business questions into reliable datasets, metric definitions, and self-serve reporting in Metabase.

- Support AI and analytics use cases. Maintain the semantic layer, metric definitions, and documentation that allow LLM-based tools and internal agents to query the warehouse accurately and consistently.

What you'll bring:

- Experience: 5+ years of professional experience in data engineering, analytics engineering, or a closely related role, ideally at a B2B SaaS or fintech company.

- SQL and Python: Expert-level SQL and strong Python skills for building pipelines, transformations, and tooling; comfortable writing tested, production-grade code.

- Modern data stack: Hands-on production experience with a cloud data warehouse (BigQuery strongly preferred), dbt or equivalent transformation framework, managed ingestion tools (Fivetran or similar), and an orchestrator (Airflow, Dagster, Cloud Composer, or similar).

- Data modeling: Deep understanding of dimensional modeling, layered warehouse architecture, and schema design, with strong opinions on grain, naming, and consistency.

- Data quality and observability: Experience implementing testing frameworks, lineage, monitoring, and alerting for data pipelines, and operating them in production including on-call.

- Engineering fundamentals: Fluency with git-based workflows, code review, CI/CD, and infrastructure-as-code; you treat data infrastructure as software.

- Ownership: A track record of taking ambiguous, high-impact problems and delivering reliable systems end-to-end, with a focus on outcomes rather than just implementation.

- Communication: Ability to explain technical trade-offs to non-technical stakeholders and drive alignment on data definitions across teams.