About the Role
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.
What You'll Actually Be Doing
•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.
You're Our Person If...
•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
Even Better If...
•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
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.
Technologies We Use
•Data: Trino, Spark, Pinot, Hive, Airflow, Iceberg, Spark, Datahub, Kafka
•Storage: S3
•Infrastructure: Terraform, AWS
•Languages: Python, Go, SQL, Scala, Typescript
Compensation
The typical base salary range for this position is $197,300 - $365,200 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $237,700 - $401,400 annually. The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.
