Lead Data Engineer, Customer Data Graphs

Salesforce•Chicago, IL

1d•Hybrid

About The Position

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. Lead Data Engineer, Customer Data Graphs Office hybrid in Seattle or Chicago As a Data Engineer at Salesforce within the Data & Analytics organization, you will collaborate with cross-functional teams to create and manage robust data solutions that support our analytics and business intelligence initiatives, building scalable and efficient data pipelines, optimizing data workflows, and ensuring data quality and reliability. You will work in a dynamic organization that engineers rigorous data pipelines that support customer data graphs, analytics, AI/ML models and systems, and more.

Requirements

8+ years of experience as a Data Engineer or in a similar role.
A related technical degree required.
Proficiency in data engineering tools and languages, such as Python, SQL, and Spark.
Strong understanding of database concepts, data modeling, and ETL processes with tools like Airflow, dbt, Informatica, etc.
Experience with cloud-based data solutions (e.g., AWS, Azure, Google Cloud).
Familiarity with data warehousing, SQL, NoSQL databases, and data integration techniques.
Experience with the Salesforce Ecosystem, specifically Data Cloud.
Problem-solving skills to troubleshoot and resolve data-related issues.
Excellent communication skills and ability to collaborate in a cross-functional environment.

Responsibilities

Data Architecture for Agentic Systems: Design and implement specialized data structures that support the use of customer data graphs, which power agentic context and memory.
Scalable Pipeline Engineering: Lead the development of robust ETL/ELT frameworks using Python and SQL. You will build highly decoupled, modular pipelines that can handle petabyte-scale data while maintaining strict data quality and lineage.
High-Performance Data for AI: Build customer identity graphs that serve data to applications and AI with sub-second performance.
Technical Mentorship: Act as a technical pillar for a specialized team of data and AI engineers, fostering technical excellence and elevating the overall skill set of the organization.
Strategic Technical Roadmap: In partnership with product managers and engineering leaders, aligning graph strategy and architecture with our broader Data360 and graph database efforts.
Operational Excellence: Establish and enforce rigorous technical standards for data quality, and latency to ensure agents provide reliable, real-time insights.
AI Integration & Automation: Lead high-impact efforts to automate the data delivery pipeline, ensuring seamless integration between internal databases, third-party APIs, and the AI orchestration layer.