Data Systems Senior Analyst — Seller Analytics

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 Enterprise Technology & Infrastructure 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. The Experience Join the Seller Analytics team in Salesforce's Digital Enterprise Technology organization. We build and run the Sales Data Mesh: our governed, certified data products for Sales, covering our Pipeline Metrics, Annual Order Value, AE and Product Participation, Product Usage, and more. Our consumers are the CRO Analytics team, sales leaders across the globe, and our partner analytics teams in Finance, Revenue, and Marketing. Our consumers rely on those data products, and this role keeps them trustworthy. You are the first responder; when a consumer reports that a number looks wrong, a dashboard is stale, or a pipeline did not land, you reproduce the problem, find the cause, and resolve-or-route it with a clear writeup. It is an investigate-and-triage role first. Over the first year we will train you to move from operating the mesh to building it, growing into a data engineering role. We are hiring for someone who wants that path. What You'll Actually Be Doing • Triage issues reported by consumers in Slack support channels. Reproduce the problem, establish the grain and definitions involved, and give a clear answer or next step. • Investigate data-quality and freshness problems across the bronze to gold layers. Trace a wrong or missing number back to the model, source, or pipeline run that caused it. • Run row-level and column-level validation and parity checks when models change or legacy logic is migrated, and confirm the results match. • Watch monitoring and alerting (Monte Carlo, dbt tests, source freshness) and act on failures. Open and track the work in Linear. • Support hypercare and UAT for launches and migrations. Verify fixes in Snowflake and keep stakeholders updated on status. • Keep definitions, grain, and known issues documented so the next question is faster to answer. Where This Role Grows • This is an operate-and-support role today, with a deliberate path into data engineering. As you build SQL and dbt depth, you will take on model changes and tests, and in time build and own mesh data products. If you want to become a data engineer, this role is built for that. You're Our Person If… • You are proficient in Tableau Desktop and/or SQL and can read, debug, and validate queries against real data. • You have an investigate-and-triage instinct. You are comfortable not knowing the answer yet and work a problem methodically until you find the cause. • You are comfortable in Snowflake or a similar cloud data warehouse. • You communicate clearly in writing and can explain a data issue, its cause, and its fix to a non-engineer. • You want to grow into data engineering and are ready to be trained on the rest. Even Better If… • You have used dbt or a similar transformation framework. • You have worked with data observability or monitoring tools (e.g. Monte Carlo). • You understand dimensional modeling, grain, and surrogate keys. • You have done migration or parity