Purpose
Dubsado is a client relationship management platform that helps creative service businesses run their operations: proposals, contracts, invoicing, scheduling, and workflow automation - all in one place. As our product and customer base grow, so does the importance of the data that powers our decisions.
We're looking for a Senior Software Engineer, Data to be our first dedicated data hire: someone who will take ownership of our data systems, make them trustworthy, and lay the foundation for a data-informed engineering and business culture. This role carries significant autonomy, trust, and influence over the technical direction of our data infrastructure.
After ten years operating as a profitable business, Dubsado has real data, real users, and real business questions that better data systems would answer. You will set the technical direction, make the tooling calls, build out the systems that don't exist yet, and define what "good data" means at Dubsado. The person who builds this out becomes the person the org turns to as the data function grows.
This job is a hybrid position in Glendale, CA without relocation assistance provided by the company.
What You’ll Work On
You’ll start by getting up to speed on our existing data stack - BigQuery, dbt, PostHog, Hevo, and Metabase - and assess where we stand. You'll build context on the short-, medium-, and long-term needs of various data stakeholders. Some of the early work will involve improving the reliability and trust of our current data pipelines, models, and warehouse.
In the medium and longer term, you’ll have an opportunity to expand your scope in a few directions. At their core, our data systems will need to be continuously evolving to provide safe, compliant, and useful context for AI agents + humans alike. We’ll need to extend our data systems to support customer-facing data features, which will impose distinct scaling and reliability challenges compared to internal use cases.
We aren't dogmatic about our existing OLAP tooling; there will be room to experiment and make the case for better approaches via emergent techniques. One example is leveraging engineering observability data (logs, metrics, traces) beyond traditional debugging and incident response.
Responsibilities
- Collaborate with engineering, product, and business teams to understand data needs and translate them into scalable, well-tested solutions
- Establish standards for the reliability, quality, and observability of Dubsado's data pipelines and warehouse
- Define and drive the technical direction and standards for Dubsado's data systems, including architecture decisions, tooling choices, and development practices
- Design and build new data infrastructure where high-leverage opportunities exist, with latitude to evaluate and introduce new tooling
- Define tools and guardrails that empower other technical collaborators to build and maintain analytical models
- Contribute across the broader software stack over time — particularly backend services, infrastructure, and internal agentic systems
- As the data function grows, transition data quality and observability from sole ownership to shared responsibility across the engineering team
Qualifications
Required
- 5+ years of experience in software engineering, with a strong focus on data engineering and/or data infrastructure
- Deep experience with data warehousing, pipeline design, and analytical modeling
- Proficiency with SQL and dbt; experience with BigQuery or similar cloud data warehouses
- Experience using AI-assisted development tools (e.g., Cursor, Claude Code) and higher-level AI orchestration in engineering workflows
- Comfort operating as the sole owner of a domain — setting direction, making tradeoffs, and communicating across technical and non-technical audiences
- Strong fundamentals in software engineering: project planning, decomposition, testing, code review, documentation
- Bachelor's in CS or equivalent professional experience.
Nice to Have
- Experience with MongoDB
- Familiarity with PostHog, Hevo, Metabase, or Observable
- Experience introducing data quality and observability practices in an organization that's still building that muscle
- Background working at a smaller company where you wore multiple hats
Benefits
- Medical, dental, and vision insurance (UnitedHealthcare & Principal)
- Employer-matched 401(k)
- Employer-sponsored life and disability insurance
- Paid parental leave
- PTO — accrued, starting at 2 weeks and increasing with tenure
- 12 days sick leave
- Company holidays, including most major federal holidays
- Office closed the week between Christmas and New Year's
- Hybrid schedule — 3 days in office
- Stocked kitchen — coffee, tea, protein shakes, sparkling drinks, and snacks (we take our snacking seriously)
Interview Process
Here's exactly what to expect if you apply, start to finish. This process takes just under 5 hours of your time. We've designed our process to make sure it's a great fit on both sides. The technical interviews will be open-book, open-internet, open-tools, emulating what you’d have on the job. Most of the process happens remotely, with one final in-person step so you can see our space, meet the team, and get a feel for the environment we work in.
- Screening Call (30 min, virtual) - A conversation with our COO to get to know you and give you a better sense of who we are as a company. This is where those bigger-picture questions about culture and fit come up.
- Technical Take-Home Project + Interview (2 hours total, virtual) - A take-home project followed immediately by a 30 minute conversation with our Director of Engineering to walk through your approach.
- Technical Interview (1 hour, virtual) - A deeper technical conversation with two senior engineering leaders on the team
- Cross-Functional Interview (45 min, virtual) - A conversation with our VP of Product Development and our BI Analyst to explore how you’d collaborate with other teams
- Final Interview (in-person) - A final conversation with our Director of Engineering, plus a chance to tour our space and meet more of the team in person.
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