Data Systems Senior Analyst — Seller Analytics

Salesforce•Seattle, WA

About The Position

Join the Seller Analytics team in Salesforce's Digital Enterprise Technology organization. The team builds and runs the Sales Data Mesh: governed, certified data products covering Pipeline Metrics, Annual Order Value, AE and Product Participation, Product Usage, and more. Consumers include CRO Analytics, sales leaders around the world, and analytics partners in Finance, Revenue, and Marketing. This role is focused first on investigating and triaging data issues reported by consumers. You will reproduce problems, trace causes across data models, sources, and pipeline runs, and resolve or route them with clear documentation. The role also supports data-quality and freshness checks, monitoring, launches, and migrations. Over the first year, the position is intended to develop from operating and supporting the data mesh toward data engineering work, including model changes, tests, and eventually building and owning 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 investigative and triage approach, including comfort working through problems without immediately knowing the answer.
Comfort with 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 proving new results match.
Experience with Airflow, Tableau, Snowflake semantic views, or Cortex.
Experience with AI-assisted engineering tools such as Claude Code.

Responsibilities

Triage issues reported by consumers 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 data layers.
Trace incorrect or missing numbers to the model, source, or pipeline run that caused them.
Run row-level and column-level validation and parity checks when models change or legacy logic is migrated, and confirm results match.
Monitor alerts and checks, including Monte Carlo, dbt tests, and source freshness, 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 updated on status.
Document definitions, grain, and known issues.
As SQL and dbt skills develop, take on model changes and tests, with the longer-term goal of building and owning mesh data products.