Employee Success Senior Data Engineer

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. Salesforce’s Employee Success (ES) Product Management team is advancing AI-driven data initiatives to expand our data capabilities across all ES functions. A key priority is developing a robust data and AI foundation to support innovation, automation, and data-driven decision-making. We are seeking a highly skilled Senior Data Engineer to build and harden our data foundations. You will be responsible for engineering end-to-end data pipelines and analytics frameworks, ensuring the seamless delivery of organizational metrics and AI-driven agents throughout the ES landscape. You will collaborate with ES business partners to translate functional requirements into scalable technical solutions, integrating diverse data sources—including Neo4j graph databases—into high-availability pipelines within Snowflake and Data360. In partnership with internal technology teams, you will engineer automated, standardized data collection processes and manage the full CI/CD lifecycle to ensure data integrity, code reliability, and operational efficiency. The Ideal Candidate brings a strong background in ETL development, advanced SQL, and data modeling, with a proven track record of building robust Snowflake foundations. They possess deep expertise in engineering scalable, repeatable data pipelines using Apache Airflow and managing automated deployment workflows. While core data engineering and rigorous QA testing are the priorities, experience with Salesforce Data360 and AI/ML integration is a highly desirable plus.

Data Engineering: Architect, develop, and optimize complex ETL/ELT pipelines using Apache Airflow and Python to ensure high-performance data delivery. Data Foundations: Design and implement robust logical and physical data models within Snowflake and Salesforce Data360 to serve as the enterprise "source of truth." Graph Development: Build and manage Neo4j data graphs, optimizing Cypher queries and data structures to map complex enterprise relationships. DevOps & CI/CD: Take ownership of CI/CD pipeline management and Git version control to automate testing, standardization, and deployment of data assets. Automated Frameworks: Design and develop reusable, repeatable automation frameworks to streamline data ingestion and transformation across the ecosystem. QA & Performance Tuning: Lead QA testing initiatives and optimize data storage, clustering, and retrieval to ensure maximum scalability and cost-efficiency. Integration Development: Build and maintain high-throughput APIs for seamless data exchange between Snowflake, AWS services (Lambda, S3), and Salesforce Data360. Stakeholder Translation: Collaborate with HR business partners to translate functional needs into technical requirements, building high-quality POCs and technical documentation. Required Skills/Experience Relevant work experience, with 6+ years related information systems Proven expertise in end-to-end data engineering, including advanced data modeling (Star/Snowflake schema) and pipeline automation; experience in People Analytics is a strong plus. Proficiency with SQL, Python, Bash, and Informatica, dbt. Deep