Staff & Senior Staff Data Platform Engineer (Slack)

Join Salesforce, the #1 AI CRM, as a Staff or Senior Staff Data Platform Engineer. In this role, you will help build Agentic Analytics at Slack, collaborating with engineers across various teams and driving the architecture, implementation, rollout, and adoption of the data platform. You will work with a range of technologies, including Trino, Spark, Pinot, Hive, Airflow, Iceberg, Kafka, S3, Terraform, AWS, Python, Go, SQL, Scala, and Typescript.

• Collaborate on technical direction, build, and go from early prototypes through production for Agentic Analytics at Slack.
• Design APIs, MCPs, and tools that let agents map data concepts to the right entities in the data warehouse.
• Drive architecture, implementation, rollout, and adoption of data platform for agents, ensuring high-quality engineering practices.

• 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
• You’re a high velocity engineer. You decompose problem spaces into digestible chunks and constantly iterate
• Strong proficiency in programming languages like Python, Go, Java, or similar
• Clear technical communication and the ability to build alignment with engineers, data scientists, analysts, and product partners
• 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
• 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
• Practical experience using AI development tools to accelerate implementation while maintaining engineering quality
• Expertise in SQL & Python to build data pipelines
• Experience building APIs, SDKs, or self-service platforms that other engineers use