AI agents — both the ones we ship to customers and the ones our engineers use to build Slack — are fundamentally changing how data gets created, queried, and acted upon. We are seeking a leader who can transform our data engineering stack from a traditional analytics platform into the foundation for agentic analytics (AI agents that autonomously explore, analyze, and surface insights from data) and agentic development (AI-powered engineering tools that use data infrastructure as their backbone).

As Senior Director of Data Engineering, you will lead a ~40-person organization and own the full data engineering stack — from infrastructure and ingestion through to data products, semantic layers, and AI-facing data services. You'll partner closely with Data Science & Analytics as a strategic peer while driving a bold technical vision: making Slack's data platform the best-in-class substrate for both human analysts and AI agents. This is not a maintenance role. We're looking for someone who sees the agentic future of data platforms and wants to build it.

What You'll Build (Transform)

• Architect the data layers agents rely on— Design and ship data APIs, MCP servers, and semantic interfaces that let AI agents (Slackbot AI, internal coding agents, customer-built Agentforce agents) query, reason over, and act on Slack's data autonomously
• Transform the semantic layer — Evolve our metrics platform from a human-query tool into a machine-readable knowledge that agents can navigate, with governed metric definitions, lineage, and natural-language access patterns
• Build real-time data products — Move beyond batch analytics to streaming data infrastructure that supports sub-second agent decision-making, real-time experimentation, and low-latency retrieval
• Ship agentic analytics tooling — Create the next generation of self-serve analytics where AI agents draft queries, detect anomalies, generate insights, and surface recommendations — replacing manual dashboard-watching with proactive, agent-driven intelligence
• Establish AI-native observability — Instrument the data stack with LLM-aware tracing (OpenTelemetry GenAI conventions), token/cost attribution, and quality metrics that treat AI agents as first-class consumers of data infrastructure
• Drive the Data MCP strategy — Own the vision for how Slack's data warehouse, metrics layer, and analytics tools are exposed to AI agents via MCP servers, making Slack's data the most agent-accessible enterprise dataset in the industry

What You'll Run (Operate)

• Own and unify the Data Engineering roadmap across infrastructure, ingestion, data governance, tooling, semantic layer sub-teams
• Serve as the DRI for data engineering, representing data engineering in leadership planning and resolving cross-team priority conflicts
• Partner directly with the Data Science & Analytics organization — establishing and running an effective operating model
• Set data freshness SLAs, warehouse reliability, and cost optimization standards across ingestion and infrastructure teams and ensure the data platform meets those standards and goals
• Build and scale engineering capacity — hire, develop and retain top EM and senior IC talents
• Champion a strong data engineering identity and culture
• Partner with product, infrastructure, and DevXP leadership on cross-cutting initiatives

Minimum Qualifications

• 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
• 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
• Demonstrated success partnering with Data Science / Analytics leadership as a peer stakeholder
• 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
• A related technical degree required

Preferred Qualifications

• 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
• Familiarity with modern data stack components: warehouse infrastructure, streaming ingestion, semantic/metrics layers (governance, OLAP systems, Airflow-like orchestration)
• Experience running experimentation platforms and A/B testing infrastructure at scale