Staff & Senior Staff Data Platform Engineer - Slack

Salesforce•San Francisco, CA

About The Position

Salesforce is seeking a Staff or Senior Staff Data Platform Engineer to help build Agentic Analytics for Slack. The role involves shaping technical direction and taking solutions from early prototypes through production across areas of the data stack, including ingestion, infrastructure, semantic layers, and interfaces that make data useful to people and AI agents. The engineer will collaborate with Data Engineering, Data Science, product, core infrastructure, and developer experience teams to iterate on high-leverage problems and deliver pragmatic solutions. The role includes building a data platform for agents through APIs, MCPs, and tools that map data concepts to warehouse entities; improving the semantic layer so people and agents can navigate metric definitions, lineage, ownership, and reliability; and developing ingestion and infrastructure for timely analytics and experimentation. The engineer will balance freshness, accuracy, reliability, and cost, and build tools that support query drafting, root-cause analysis, and visualization. They will own architecture, implementation, rollout, and adoption, while raising engineering standards through technical guidance, sound design decisions, dependency resolution, and mentorship. The successful candidate works iteratively and at high velocity, prototypes to test ideas and gather feedback, builds context in unfamiliar areas, and balances rapid experimentation with production-grade reliability and scalability.

Requirements

At least 8 years of experience building and operating a data platform.
A track record of leading complex technical projects across teams as an individual contributor.
Strong software engineering skills combined with deep data expertise.
Clear technical communication and the ability to build alignment with engineers, data scientists, analysts, and product partners.
Strong proficiency in a programming language such as Python, Go, or Java, or a similar language.

Nice To Haves

Experience building infrastructure or tools for AI agents.
Experience with semantic layers, metrics platforms, data catalogs, or lineage, including familiarity with tools such as Airflow, dbt, Databricks, sqlmesh, DataHub, Superset, or Snowflake.
Expertise in SQL and Python for building data pipelines.
Experience building APIs, SDKs, or self-service platforms used by other engineers.
Practical experience using AI development tools to accelerate implementation while maintaining engineering quality.

Responsibilities

Build a data platform for agents, including APIs, MCPs, and tools that map data concepts to the right entities in the data warehouse.
Improve the semantic layer so people and agents can navigate metric definitions, lineage, ownership, and reliability.
Build ingestion and infrastructure for timely analytics and experimentation.
Evaluate tradeoffs among data freshness, accuracy, reliability, and cost.
Develop tools that help users draft accurate queries, perform root-cause analysis, and build visualizations.
Own architecture, implementation, rollout, and adoption through production.
Lead complex technical projects across teams as an individual contributor.
Help engineers make sound design decisions, resolve technical dependencies, and grow through mentorship and example.
Prototype, iterate quickly, and build reliable, scalable production systems.