Staff Data Engineer - Slack

Leadout Capital; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Slack is seeking a Staff Data Engineer to set the metrics platform direction, govern data quality, and guide AI-driven analytics. You will own data modeling, semantic layers, and scalable pipelines, mentoring engineers across teams while delivering measurable impact.

In this senior individual contributor role, you will partner with Product, Data Science, Analytics, and Engineering to define metric stewardship, SLAs, and cost-aware architectures that scale with company needs.

Qualifications

• 10+ years of software or data engineering experience
• 6+ years hands-on with data architecture, data modeling, and metadata management
• Led multi-team technical initiatives as an IC
• Deep SQL expertise and Python skills; working knowledge of Go/Java/Scala
• Experience with Spark, Trino, Airflow, Iceberg, Snowflake; dimensional modeling

Responsibilities

• Set architecture and roadmap for metrics platform, balancing freshness, accuracy, reliability, and cost
• Design and scale data models and pipelines turning billions of records into trusted datasets with SLA signals
• Evolve semantic layer and build APIs/tools for metric definitions, lineage, and queries
• Lead metric certification, governance, and design reviews; mentor senior engineers using AI-assisted development

Skills

SQL expertise

Python

Go/Java/Scala

Data modeling

Metadata management

Spark

Trino

Airflow

Iceberg

Snowflake

Education

Related technical degree

Tools

dbt

DataHub

Cube

Job description

The Experience

The Metrics Foundations team sits within Slack's Data Engineering organization in Data & Insights, building the trusted datasets and metrics that power 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 (SLAs).

Increasingly, AI agents and developer tools consume these metrics alongside people, so you build the trusted, machine-readable foundation that maps business questions to the right metric definitions and tables. As a Staff Data Engineer, you set the technical direction for how Slack defines, computes, governs, and serves its most critical metrics, and you serve 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.

What You'll Actually Be Doing

• Set the architecture and roadmap for the metrics platform, from ingestion and canonical datasets to the semantic layer and serving, making clear tradeoffs between 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 the APIs and tools that let people and AI agents navigate metric definitions, lineage, and ownership, and draft accurate queries.
• Lead metric certification, governance, and design reviews, and mentor senior engineers, using AI-assisted development to deliver faster without compromising quality.

You're Our Person If...

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

Even Better If...

• You have experience building infrastructure or tools for AI agents, such as Model Context Protocol (MCP) servers, tool-use APIs, or evaluation harnesses.
• You have experience with semantic layers, metrics platforms, data catalogs, or lineage tools such as dbt, sqlmesh, Datahub, Cube, or LookML.
• You have experience with experimentation platforms, including metrics, exposure logging, and guardrails for A/B testing.
• You prototype early, balance speed with judgment, and measure your impact by what the teams around you ship.