Seller Analytics Engineer

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.

Responsibilities

• 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.

Growth Opportunities

• 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.

Requirements

• Proficient in Tableau Desktop and/or SQL and can read, debug, and validate queries against real data.
• An investigate-and-triage instinct. Comfortable not knowing the answer yet and work a problem methodically until finding the cause.
• Comfortable in Snowflake or a similar cloud data warehouse.
• Clear communication in writing and ability to explain a data issue, its cause, and its fix to a non-engineer.
• Willingness to grow into data engineering and be trained on the rest.

Nice to Have

• Experience with dbt or a similar transformation framework.
• Familiarity with data observability or monitoring tools (e.g., Monte Carlo).
• Understanding of dimensional modeling, grain, and surrogate keys.
• Experience with migration or parity work: replacing legacy logic and proving new results match.
• Knowledge of Airflow, Tableau, or Snowflake semantic views and Cortex.
• Experience with AI-assisted engineering tools such as Claude Code.

Benefits

• Time off programs
• Medical, dental, vision
• Mental health support
• Paid parental leave
• Life and disability insurance
• 401(k)
• Employee stock purchasing program