Senior Director of Data Platform (Slack)

Join Slack as the Senior Director of Data Platform, where you will lead a ~40-person organization and own the full data stack. You will partner closely with Data Science & Analytics, drive a bold technical vision, and architect the data layers that AI agents rely on. Your responsibilities will include transforming the semantic layer, building real-time data products, shipping agentic analytics tooling, establishing AI-native observability, and driving the Data MCP strategy. You will also unify the Data Platform roadmap, serve as the DRI for Data Platform, and build and scale engineering capacity.

• Lead and manage a ~40-person organization responsible for the full data stack, from infrastructure and ingestion to data products and AI-facing data services.
• Architect the data layers that agents rely on, designing and shipping data APIs, MCP servers, and semantic interfaces that enable AI agents to query and act on Slack’s data autonomously.
• Drive the Data MCP strategy, owning the vision for how Slack’s data warehouse, metrics layer, and analytics tools are exposed to AI agents, making Slack’s data the most agent-accessible enterprise dataset in the industry.

• A related technical degree required
• Proven track record building and scaling data platforms (ingestion pipelines, warehousing, semantic/metrics layers) at consumer or enterprise SaaS scale
• Experience leading through organizational change — team consolidations, re-orgs, or multi-team integrations
• 10+ years of experience in data engineering, data platform, or infrastructure engineering roles, including 5+ years in engineering leadership at the Director level or above
• Strong track record of hiring, developing, and retaining engineering managers and senior ICs
• Excellent cross-functional communication skills, with experience presenting technical vision and strategy to executive stakeholders
• Demonstrated success partnering with Data Science / Analytics leadership as a peer stakeholder
• Experience building data platforms that serve AI/ML workloads — not just dashboards and reports, but data infrastructure optimized for model training, feature serving, RAG retrieval, or agent-driven queries
• Hands-on understanding of how LLMs and AI agents consume data — including semantic layers, embeddings, vector search, tool-use patterns (MCP, function calling), and structured vs. unstructured data access
• Experience with agentic systems, AI-assisted analytics, or building developer tools powered by AI (e.g., AI coding assistants, automated data quality, natural-language-to-SQL)
• Track record shipping data-as-a-product — APIs, SDKs, or self-serve platforms where internal or external developers are the primary consumers
• Experience operating a "pod" or embedded working model that pairs engineering with data science/analytics
• Experience running experimentation platforms and A/B testing infrastructure at scale
• Familiarity with modern data stack components: warehouse infrastructure, streaming ingestion, semantic/metrics layers (governance, OLAP systems, Airflow-like orchestration)