Software Engineering LMTS - Data; Remote

Engineering at Qualified is a high-ownership, high-velocity environment where engineers ship customer-facing product to real enterprises 're building at the frontier of AI + product: developing agentic software and embedding AI directly into our platform, using modern tool chains and processes that help engineers move faster while staying focused on high-impact problem solving.

Following our acquisition by Salesforce, we continue to operate with the pace and ownership of a startup—backed by the distribution, resources, and long‑term runway of a global enterprise. Engineers have true end‑to‑end ownership from idea to production: scoping, implementation, testing, deployment, monitoring, and iterative improvement based on real customer feedback, without layers of approvals slowing you down.

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, personalise, 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.

• 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

• 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 (Click House or similar), CDC and event streaming, ETL orchestration, or data modelling 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 energising
• 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)

• Experience supporting AI/ML or agentic products, where data freshness and grounding directly shape model behaviour
• Experience with enterprise SaaS (especially B2B marketing or sales tech)
• Experience with real‑time or event‑driven systems
• Sta…