Staff & Senior Staff Data Platform Engineer - Slack

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

Job summary

Slack is seeking a Staff or Senior Staff engineer to help build out Agentic Analytics. You will collaborate on technical direction, build, and scale from prototypes to production.

Your work spans ingestion, infrastructure, semantic layers, and human-friendly interfaces for data and AI agents. You will partner with engineers across Data Engineering, Data Science, product, infrastructure, and developer experience to iterate on high-leverage problems and ship pragmatic solutions.

Qualifications

• 8+ years of experience building and operating a data platform.
• Track record of leading complex technical projects across teams as an IC.
• Strong software engineering skills balanced with deep data expertise.
• Clear technical communication and ability to align engineers, data scientists, analysts, and product partners.

Responsibilities

• Build data platform for agents. Design APIs, MCPs, and tools to map data concepts to data warehouse entities.
• Improve semantic layer for metric definitions, lineage, ownership, and reliability.
• Develop ingestion/infrastructure for timely analytics and experimentation; balance freshness, accuracy, reliability, and cost.
• Create tools to draft accurate queries, perform root-cause analysis, and build visualizations.
• Own the path to production: architecture, rollout, and adoption.
• Raise engineering bar through mentorship and leading by example.

Skills

Data platform

Leadership

Python

Go

Java

Communication

Mentorship

Tools

Airflow

dbt

Databricks

Snowflake

Job description

Agents are changing how people build software and analyze data. Slack’s data platform needs to support both agents that surface insights, and developer tools that use data to provide quantitative feedback in the product iteration process.
We’re looking for a Staff or Senior Staff engineer to help build out Agentic Analytics at Slack. You’ll collaborate on technical direction, build, and go from early prototypes through production. Your work could span many parts of the data stack: ingestion, infrastructure, semantic layers, and the interfaces that make data useful to humans and AI agents.
You’ll partner with engineers across Data Engineering, Data Science, product, core infrastructure, and developer experience to quickly iterate on high leverage problem spaces and ship pragmatic solutions.
You’ll help us:

• Build data platform for agents. Design APIs, MCPs, and tools that let agents map data concepts to the right entities in the data warehouse.
• Make data digestible. Improve our semantic layer so people and agents alike can navigate metric definitions, lineage, ownership, and reliability.
• Make data on time (or provide signal when it’s not). Build ingestion and infrastructure for timely analytics, and experimentation. Evaluate tradeoffs between freshness, accuracy, reliability, and cost.
• Ship tools that make data easy to analyze. Make it easy to draft accurate queries, do root cause analysis, and build visualizations.
• Own the path to production. Drive architecture, implementation, rollout, and adoption.
• Raise the engineering bar across teams by helping engineers make good design decisions, resolve technical dependencies, and grow through mentorship and leading by example.

How you work matters:

• You’re a high velocity engineer. You decompose problem spaces into digestible chunks and constantly iterate.
• You’re biased toward action. You love to prototype to test ideas and get feedback early, but are okay throwing away bad ideas.
• You can quickly build context in unfamiliar areas. You can move between a pipeline, a service, or a developer tool, build enough context to contribute. You also know when to ask for help when it speeds things up.
• You balance speed and judgment. You can get a prototype off the ground quickly, but also build a reliable and scalable production system

Minimum qualifications:

• 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 balanced with deep data expertise
• Clear technical communication and the ability to build alignment with engineers, data scientists, analysts, and product partners
• Strong proficiency in programming languages like Python, Go, Java, or similar

Bonus points for:

• Experience building infrastructure or tools for AI agents
• Experience with semantic layers, metrics platforms, data catalogs, or lineage. These might include familiarity with tools like Airflow, dbt, Databricks, sqlmesh, Datahub, Superset or Snowflake
• Expertise in SQL & Python to build data pipelines
• Experience building APIs, SDKs, or self-service platforms that other engineers use.
• Practical experience using AI development tools to accelerate implementation while maintaining engineering quality.

Technologies We Use:
Data: Trino, Spark, Pinot, Hive, Airflow, Iceberg, Spark, Datahub, Kafka

Storage: S3

Infrastructure: Terraform, AWS

Languages: Python, Go, SQL, Scala, Typescript