Software Engineering LMTS - Data (Remote)

Salesforce•San Francisco, CA

5d•$172,500 - $285,800•Remote

About The Position

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. The Engineering team at Qualified operates in a high-ownership, high-velocity environment, shipping customer-facing product to real enterprises at scale. Following its acquisition by Salesforce, Qualified continues to operate with the pace and ownership of a startup, backed by the resources of a global enterprise. Engineers have end-to-end ownership from idea to production, including scoping, implementation, testing, deployment, monitoring, and iterative improvement based on customer feedback. As an engineer on the Data Team, you will be a technical leader on the foundational layer of the platform. The team owns Qualified's data end-to-end, including leads and accounts data models, ETL pipelines, Data 360, Salesforce sync, analytics and insights, data integrations, and Data APIs. This role is on a newer team with significant greenfield opportunities, where you will shape the architecture of core pipelines and data models, set patterns for other teams, and transform inherited systems into a coherent platform. You will also play a direct role in enhancing Piper's intelligence by ensuring the data she can access is correct and fresh, which is crucial for closing enterprise deals.

Requirements

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 Haves

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

Responsibilities

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