Data Systems Senior Analyst — Seller Analytics

Salesforce•Seattle, WA

2d•$99,400 - $150,300

About The Position

Join the Seller Analytics team in Salesforce's Digital Enterprise Technology organization. The team builds and operates the Sales Data Mesh, a collection of governed, certified data products for Sales, including Pipeline Metrics, Annual Order Value, AE and Product Participation, and Product Usage. Its consumers include the CRO Analytics team, sales leaders around the world, and analytics teams in Finance, Revenue, and Marketing. This role helps keep those data products trustworthy by investigating and triaging consumer-reported issues. You will reproduce problems, identify causes, and resolve them or route them with a clear written explanation. The role begins with operating and supporting the data mesh, with training and a planned path toward data engineering. As you develop SQL and dbt skills, you will take on model changes and tests and eventually build and own mesh data products.

Requirements

Proficiency in Tableau Desktop and/or SQL, including the ability to read, debug, and validate queries against real data.
A methodical investigate-and-triage approach and comfort working through problems when the answer is not initially known.
Comfort working in Snowflake or a similar cloud data warehouse.
Clear written communication and the ability to explain a data issue, its cause, and its fix to a non-engineer.
Interest in growing into data engineering and readiness to be trained.

Nice To Haves

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

Responsibilities

Triage consumer-reported issues in Slack support channels, reproduce problems, establish the relevant grain and definitions, and provide a clear answer or next step.
Investigate data-quality and freshness problems across bronze-to-gold layers and trace incorrect or missing data to the responsible model, source, or pipeline run.
Run row-level and column-level validation and parity checks when models change or legacy logic is migrated, and confirm that results match.
Monitor alerts and data-quality signals from Monte Carlo, dbt tests, and source freshness checks, and act on failures.
Open and track work in Linear.
Support hypercare and user acceptance testing for launches and migrations, verify fixes in Snowflake, and keep stakeholders informed of status.
Document definitions, data grain, and known issues.
As skills develop, take on model changes and tests and work toward building and owning data-mesh products.