To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.
Job Category
Software Engineering
Job Details
The Role
As an engineer on the Data Team, you'll operate as a technical leader on the layer everything else is built on. The team owns Qualified's data end to end: leads and accounts data models, ETL pipelines, Data 360, Salesforce sync, analytics and insights, data integrations, and Data APIs. Every product area and every AI agent we ship depends on this layer being correct, fresh, and fast.
This is a newer team with real greenfield ahead. You'll shape the architecture of core pipelines and data models, set patterns that other teams build against, and turn a set of inherited systems into a coherent platform. You'll also play a direct role in Piper's intelligence: her ability to reason, personalize, and act is bounded by the data she can see, and the enterprise deals we're closing depend on how confidently that data moves between Qualified and Salesforce.
What You'll Do
- Design and ship data systems end-to-end: models, pipelines, sync, and serving layers that the whole platform builds on
- Drive technical design for high-volume, multi-component systems (ETL, event streaming, analytics stores, APIs) with an emphasis on correctness, freshness, and operational excellence
- Own Salesforce data flows at enterprise scale
- Build the analytics and insights infrastructure that powers customer-facing reporting and internal decision-making
- Define data quality standards: SLOs, data contracts, observability, and the tooling to enforce them
- Own delivery from scope/design through launch, monitoring, and iterative improvement—anticipating risks, sequencing work, and aligning stakeholders
- Partner closely with product teams and engineering leadership to make data a first-class platform capability
- Raise team effectiveness through mentoring, technical guidance, code review, and improving standards, tooling, and processes
Who You Are
- Proven experience building and operating production data systems at scale: pipelines, data models, or high-volume backend services
- Strong depth in several of: PostgreSQL at scale, OLAP systems (ClickHouse or similar), CDC and event streaming, ETL orchestration, or data modeling for CRM-shaped domains
- Demonstrated technical leadership: you influence architecture and execution beyond your immediate workstream
- Strong track record delivering complex projects with multiple stakeholders, dependencies, or phases
- Comfortable operating in ambiguity and driving clarity: a newer team means the roadmap and conventions are still being written, and you find that energizing
- Pragmatic about reliability: you think in terms of data contracts, backfills, idempotency, and what happens when things fail
- Experience with modern stacks (Rails/Postgres/AWS is a plus; adaptability matters most)
Nice to Have
- Experience supporting AI/ML or agentic products, where data freshness and grounding directly shape model behavior
- Experience with enterprise SaaS (especially B2B marketing or sales tech)
- Experience with real-time or event-driven systems
- Startup experience and rapid shipping
Why Join Qualified
- Build the layer everything depends on: Own the data systems that power every product area and every AI agent we ship, with impact that compounds across the platform.
- Real greenfield, real constraints: Shape architecture and patterns on a newer team while serving production traffic for major enterprises from day one.
- AI as a core product surface: Piper's intelligence is bounded by the data she can reason over. Your work directly raises that ceiling.
- Salesforce ecosystem leverage: Define how Qualified data moves into Salesforce Data Cloud and the broader ecosystem, unlocking enterprise expansion.
- High bar, low bureaucracy: Work with a team that values craft and decisive execution, where strong ideas win and shipping is the default.
If you're excited to build the data platform behind an AI-powered product at enterprise scale, we'd love to hear from you.
