Staff & Senior Staff Data Platform Engineer - Slack

Slack•Atlanta, GA

4d•$197,300 - $401,400

About The Position

Salesforce is hiring 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 teams across Data Engineering, Data Science, product, core infrastructure, and developer experience to iterate on high-impact problems and deliver pragmatic solutions. The position includes designing APIs, MCPs, and tools that help agents map data concepts to warehouse entities; improving semantic layers for metrics, lineage, ownership, and reliability; building timely analytics and experimentation infrastructure; and developing tools for query drafting, root-cause analysis, and visualization. The engineer will own architecture, implementation, rollout, and adoption, and help raise engineering standards through technical guidance, sound design decisions, dependency resolution, and mentorship. The role calls for a high-velocity, action-oriented engineer who can rapidly build context, prototype and iterate, and balance speed with production reliability and scalability. Salesforce lists technologies used by the team across data, storage, infrastructure, and programming languages, including Trino, Spark, Pinot, Hive, Airflow, Iceberg, Datahub, Kafka, S3, Terraform, AWS, Python, Go, SQL, Scala, and Typescript.

Requirements

At least 8 years of experience building and operating a data platform.
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 skills and ability to build alignment with engineers, data scientists, analysts, and product partners.
Strong proficiency in a programming language such as Python, Go, Java, or similar.

Nice To Haves

Experience building infrastructure or tools for AI agents.
Experience with semantic layers, metrics platforms, data catalogs, or lineage; 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 data platform capabilities for agents, including APIs, MCPs, and tools that map data concepts to the right warehouse entities.
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, evaluating freshness, accuracy, reliability, and cost tradeoffs.
Develop tools that make data easier to analyze, including tools for drafting accurate queries, root-cause analysis, and visualizations.
Own architecture, implementation, rollout, and adoption of solutions through production.
Collaborate with Data Engineering, Data Science, product, core infrastructure, and developer experience teams to iterate and ship solutions.
Guide engineers in making good design decisions, resolve technical dependencies, and support their growth through mentorship and leading by example.
Prototype quickly, gather feedback, and iterate while building reliable and scalable production systems.