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Salesforce

Senior Director, Data Engineering - Slack

  • Office
  • 6+ years

Salary

Not stated

Similar roles pay $185K - 225K a year · our estimate

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Description

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.

Job Category

Software Engineering

Job Details

About Salesforce

Salesforce is the 1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.

Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.

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

Unleash Your Potential

When you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best , and our AI agents accelerate your impact so you can do your best . Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future — but to redefine what’s possible — for yourself, for AI, and the world.

Where you’d work

From the office

About the company

Salesforce

  • Industry: SaaS

Offices in San Francisco, United States, New York, United States, Atlanta, United States, Seattle, United States, United States

Also hiring in Chicago, United States, Dallas, United States, Bellevue, United States and 31 more places

55 of their 340 open roles are remote

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  • Your field
  • Level
  • Stack
  • Work model
  • Salary floor
  • Must-haves
Details12 facts · Role, Location, Compensation, Employment, Company
Tech stack
  • SQL
  • Airflow
  • LLMs
  • RAG
  • Vector databases
Seniority
Director
Type
Full-time
Industry
SaaS
Specialty
Data Engineering
Region
United States
Show 6 more factsShow less

Role

Category
Data & Analytics
Specialty
Data Engineering
Seniority
Director
Experience
10+ years
Tech stack
  • SQL
  • Airflow
  • LLMs
  • RAG
  • Vector databases

Location

Work model
Office
Region
United States
Offices
  • San Francisco, United States
  • New York, United States
  • Atlanta, United States
  • Seattle, United States
  • United States

Compensation

Salary
Salary by agreement
Pay period
Annual

Employment

Type
Full-time

Company

Industry
SaaS

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