Principal Data Engineer

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Job Category: Data

Job Details

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. Agentforce is the future of AI, and you are the future of Salesforce.

Key Role Responsibilities

• Design and implement a robust data model that integrates data from core B2B systems, including Snowflake, Salesforce Data 360, multiple Salesforce orgs, Informatica MDM, and Amazon data lakes.
• Design and evolve scalable end-to-end data architecture; define standards for data modeling, ingestion framework, pipelines, data quality, etc.
• Architect tables and views to clearly define and calculate critical metrics (e.g., lead conversion, MQL, marketing driven pipe, ROI).
• Translate business needs for marketing performance measurement, customer segmentation, targeting, and personalization into precise data requirements and model designs. Translate functional and non-functional requirements (e.g., analytical performance, query latency, automation throughput) into optimal logical, conceptual, and physical data model designs.
• Partner with Data Engineering to design data models that leverage advanced Snowflake features (e.g., clustering keys, materialized views, micro-partitions, time travel) to optimize query performance and cost efficiency.
• Master the benefits and trade-offs of modeling on each platform, such as leveraging Snowflake's zero‑copy data sharing vs. federating queries to S3.
• Enforce rigorous data cataloging and metadata standards to ensure all marketing metrics have a single, unambiguous definition across the organization.
• Collaborate with other Data and Application Architects to ensure the data warehouse model aligns with the overall enterprise data strategy and upstream/downstream system architectures.
• Ensure the data model is intuitive and accessible for all Data Scientists, Analysts, Data and BI Engineers who build curated datasets, predictive models, and dashboards to measure and optimize marketing performance.

Required Qualifications

• Master's or Ph.D in Computer Science, Information Systems, or a related quantitative field.
• 10+ years of hands‑on data modeling, data architecture, or database design experience.
• 5+ years of experience designing and implementing large‑scale Enterprise Data Warehouses.
• Expert‑level knowledge of dimensional modeling (Star/Snowflake schemas) and its application to business intelligence, reporting, and machine learning workloads including feature engineering for attribution models, lead scoring, and propensity models.
• Extensive experience with marketing data domains (e.g., campaign management, CRM, web analytics, attribution/marketing mix modeling, propensity modeling, forecasting, and optimization). Demonstrated ability to model complex business processes, including slowly changing dimensions and historical data tracking.
• Proven, hands‑on experience building and optimizing data models on a modern, cloud‑native data warehouse platform, with deep expertise in Snowflake.
• Advanced proficiency with SQL and DDL/DML, especially optimized for the Snowflake ecosystem. Familiarity with ETL tools (e.g., dbt, Fivetran), cloud services (AWS, GCP or Azure), and how to design data models that optimize their performance.
• Expert‑level mastery of all major data modeling methodologies and implementation trade‑offs between them such as 3NF (for applications), Data Vault (for integration layers), and Star/Snowflake schemas (for data science).
• Deep experience modeling Master Data Management golden records and hierarchies, and integrating them with operational and analytical systems (e.g., Informatica MDM).
• Experience implementing Data Mesh principles: domain ownership of data products, "data as a product" mindset with clear SLAs and documentation, and federated governance that balances central standards with domain autonomy.
• Experience designing data models that support ML feature engineering, including feature stores and feature registries. Understanding of how data modeling decisions impact feature freshness, model training pipelines, and real‑time inference.
• A proven track record of partnering directly with Data Engineering, Data Science, and Machine Learning Engineering teams to deliver data models that meet their specific needs. Must thrive in a high‑velocity environment with rapid iteration cycles and be able to balance governance requirements with engineering agility.
• Experience partnering with Data Governance teams to ensure models are compliant, secure, and integrated with the enterprise data catalog.
• Exceptional communication skills. The ability to lead technical design discussions and articulate complex technical concepts and implementation trade‑offs to both technical and business stakeholders.
• Highly organized and meticulous, with a passion for data accuracy and structural integrity.

Preferred Qualifications

• Knowledge of Salesforce Data 360 platform with experience designing, deploying, and managing data model objects on enterprise deployments of Salesforce Data 360 is highly desirable.
• Deep understanding of the data modeling challenges within a multi‑org Salesforce CRM environment and a customer activation platform (Salesforce Data Cloud canonical model DLO/DMO).

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.

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Posting Statement

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In the United States, compensation offered will be determined by factors such as location, job level, job‑related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. Salesforce offers a variety of benefits to help you live well including: time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program. More details about company benefits can be found at Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records. At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions. The typical base salary range for this position is $197,300 – $313,700 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $237,700 – $344,700 annually. The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.