Data Engineer - Senior/Lead

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. We are looking for a Data Engineer who thrives at the intersection of data engineering and analytics. In this role you will partner closely with data analysts and strategy experts to turn raw, distributed data into trusted, well-modeled datasets that power product strategy. Our teams are made up of data scientists, engineers, and strategy lead who drive product strategy with data-driven insights. We work alongside executives, product managers, customer strategy, and sales strategy partners to discover new opportunities for growth, experiment with data, drive adoption, and surface insights that shape what we build next.

Sit alongside data scientists and strategy leads in planning, design reviews, and roadmap discussions — treating their questions and hypotheses as first-class inputs to architecture decisions. Translate analytical and statistical requirements into well-performing SQL and scalable pipelines, and coach partners on patterns that scale (windowing, partitioning, incremental loads, idempotency). Own the technical solution design and architecture of data acquisition and integration projects (batch and real-time), implementing a layered stack — raw → cleansed → curated → semantic — that ensures high data quality, predictable freshness, and timely insights. Craft design artifacts (functional design documents, data flow diagrams, data models, schema contracts) that the broader team can review, extend, and rely on. Build the data pipelines, curated marts, semantic layers, and feature stores that let analysts answer business questions independently and let data scientists iterate on features and models without re-engineering raw sources. Design tailored data structures (fact/dimension models, wide analytical tables) and end-to-end infrastructure for data science work: feature pipelines, model-ready training datasets, experimentation data, and the plumbing required for reliable ML and statistical workflows. Take exploratory analyst/DS notebooks and prototypes and reinvent them as production-ready, monitored data flows. Co-own data quality, lineage, and trust with your partners, and establish shared conventions — naming, documentation, testing, and review — that make handoffs low-friction. Proactively identify gaps in data quality and performance, integrate data from disparate sources, and advocate for architectural and code improvements that improve execution speed and reliability. Perform data profiling, sophisticated sampling, statistical testing, and reliability testing on data. Serve as a domain expert and mentor for ETL/ELT design, dimensional modeling, and big data patterns; evaluate technology trade-offs and run proofs of concept to inform tooling decisions. Bring strong SQL optimization and performance tuning expertise in high-volume, parallel-processing environments, working with the team's stack: SQL, Python, Airflow, AWS, Spark, Tableau, Hadoop (and adjacent tools like dbt, Snowflake, and Databricks where they fit). Participate in the team's on-call rotation to address production data issues in real time and keep services operational and highly available for