Lead 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. Lead Data Engineer 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. The Customer Success Data Platform organization sits at the intersection of Automation, Analytics, and AI, pushing the boundaries of Salesforce technology as Customer 0 and providing trusted data that supports the AI Human tandem. What You’ll Do Architect Multi-Stage Medallion Pipelines: Design and oversee the implementation of a robust Medallion Architecture (Bronze, Silver, and Gold layers), ensuring seamless transitions from raw ingestion to highly refined, business-logic-heavy data assets. Lead Semantic Layer Strategy: Own the development and governance of the Semantic Layer , defining standardized metrics and dimensions to ensure consistent interpretation of KPIs across all downstream business intelligence and reporting platforms. Advanced Dimensional Modeling: Lead the transition from flat tables to sophisticated Star and Snowflake schemas. Design high-performance data models that support complex relationship mapping, historical versioning (SCD Type 2), and high-concurrency querying. Engineering Identity & Entity Resolution: Build and maintain scalable pipelines for identity resolution, stitching together disparate data points across the Salesforce ecosystem to create unified, governed customer and product graphs. Infrastructure for Causal & Attribution Modeling: Partner with Decision Scientists to engineer the specialized data structures and features required for advanced attribution and causal inference, ensuring these models are backed by high-integrity, production-ready data. Data Contracts & Quality Governance: Establish and enforce strict Data Contracts between upstream producers and downstream consumers. Implement automated validation frameworks to monitor data freshness, schema drift, and semantic accuracy. Operational Excellence & CI/CD: Drive engineering best practices by implementing rigorous CI/CD workflows for data pipelines, emphasizing automated testing, version control for SQL/Python transformations, and proactive performance tuning. Cross-Functional System Design: Collaborate with Product Managers and Engineering leaders to translate complex business requirements into scalable technical architectures, prioritizing data lineage, transparency, and low-latency access to trusted metrics. Qualifications: 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. Experi ... (truncated, view full listing at source)