Staff Data Engineer - Slack

Salesforce•San Francisco, CA

About The Position

The Metrics Foundations team within Slack’s Data Engineering organization in Data & Insights builds trusted datasets and metrics used for decisions across Product, Analytics, Marketing, Sales, and Operations. The team partners with Product, Data Science, Analytics, and Engineering to define and govern metrics with consistent quality and clear service level agreements. As AI agents and developer tools increasingly consume these metrics, the team builds a trusted, machine-readable foundation that maps business questions to the appropriate metric definitions and tables. The Staff Data Engineer sets technical direction for how Slack defines, computes, governs, and serves its most critical metrics, and acts as the technical lead for Agentic Analytics on the metrics side. This is a senior individual contributor role with organization-wide scope and multi-quarter impact.

Requirements

10+ years of software or data engineering experience, including 6+ years of hands-on experience with data architecture, data modeling, and metadata management.
Experience leading complex, multi-team technical initiatives as an individual contributor and driving alignment across Engineering, Product, Data Science, and Analytics without formal authority.
Deep SQL expertise and strong Python skills.
Working proficiency in Go, Java, or Scala.
A record of architecting scalable, cost-efficient data models and pipelines.
Hands-on experience with Spark, Trino, Airflow, Iceberg, and Snowflake.
Experience with dimensional and star/snowflake modeling.
A related technical degree.

Nice To Haves

Experience building infrastructure or tools for AI agents, such as Model Context Protocol (MCP) servers, tool-use APIs, or evaluation harnesses.
Experience with semantic layers, metrics platforms, data catalogs, or lineage tools such as dbt, sqlmesh, Datahub, Cube, or LookML.
Experience with experimentation platforms, including metrics, exposure logging, and guardrails for A/B testing.
Prototypes early, balances speed with judgment, and measures impact by what surrounding teams ship.

Responsibilities

Set the architecture and roadmap for the metrics platform, spanning ingestion, canonical datasets, the semantic layer, and serving, and make tradeoffs among freshness, accuracy, reliability, and cost.
Design and scale data models and pipelines that turn billions of records into trusted datasets with measurable quality, predictable SLAs, and freshness signals for consumers.
Evolve the semantic layer and build APIs and tools that help people and AI agents navigate metric definitions, lineage, and ownership, and draft accurate queries.
Lead metric certification, governance, and design reviews.
Mentor senior engineers and use AI-assisted development to deliver faster without compromising quality.