Senior Director, Data Engineering - Slack

Salesforce•Seattle, WA

1d•$218,400 - $401,400

About The Position

Salesforce is seeking a leader to transform its 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.

Requirements

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

Nice To Haves

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

Responsibilities

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
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