Senior Director of Data Engineering (Slack)

Salesforce; San Francisco (CA); On-site; USD 210,000 - 320,000; Full time

Benefits offered by this job

Medical Care

Life Insurance

Retirement Savings

Employee Assistance Programs

13 paid days off

Job summary

Salesforce is seeking a Senior Director of Data Engineering to lead a ~40-person organization and own the full data engineering stack, from infrastructure to data products and AI-facing services.

You will partner with Data Science & Analytics to drive a bold vision for Slack’s data platform and create agent-ready data platforms that power AI agents and developers.

Qualifications

• Ten+ years in data engineering roles with 5+ years in leadership (Director+).
• Proven ability to scale data platforms for AI/ML workloads across SaaS.
• Strong collaboration with Data Science and Analytics leadership.

Responsibilities

• Architect data layers accessed by AI agents; design data APIs and semantic interfaces.
• Evolve metrics/semantic layer to machine-readable knowledge with lineage and NL access.
• Build real-time data products enabling sub-second agent decisions.
• Develop self-serve analytics and agent-driven insights tools.
• Define AI-native observability, tracing, and cost attribution.

Skills

Data platforms

Engineering leadership

Hiring & mentoring

Cross-functional comms

AI/ML data workflows

LLMs & AI agents

Experimentation & A/B tests

Cloud data stack

Stakeholder alignment

Data governance

Education

Related technical degree

Tools

MCP servers

Airflow-style orchestration

Job description

• 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
• 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

Benefits

• Medical Care
• Life Insurance
• Retirement Savings
• Employee Assistance Programs
• With 9 standard holidays and four floating holidays, you get a total 13 paid days off each year

Requirements

• 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)