Data Analyst Jobs in Des Moines, WA

About The Position

Requirements

• At least 3 years in a customer-facing technical role, such as support engineering, solutions engineering, TAM, or data analyst, at a SaaS or security company.
• Startup experience and comfort with ambiguity, moving quickly, and taking ownership without waiting to be asked.
• A proactive mindset and ability to identify problems and improvements before they become customer issues.
• Working SQL skills, including writing and adapting queries in Athena or a similar engine.
• Ability to read, write, and test regular expressions and explain their false-positive behavior.
• Git and code literacy, including understanding Git and pull requests and reading and following code such as a Python script; software development experience is not required.
• Clear, structured communication skills for explaining technical results to customers and writing actionable handoffs.
• Must reside on the East Coast of the USA.

Nice To Haves

• Familiarity with data classification, DLP, or DSPM concepts, including PII, PCI, PHI, data classes, and false-positive rates.
• Exposure to cloud data stores such as S3, Snowflake, Databricks, or Microsoft 365, and understanding how sensitive data appears in them.
• Experience with dashboards such as Metabase and ticketing tools such as Shortcut or Pylon.

Responsibilities

• Be the first point of contact for classification requests from US customers and PoVs, working with Sales Engineers, Solutions Architects, TAMs, Support, and Customer Success.
• Own classification on PoV engagements by validating results before customer calls and joining calls as the classification expert.
• Tune classification using approved procedures, including adding and removing keywords, building customer-specific regex and dictionary classes, running re-analysis, reviewing customer-built classes, and managing allowlists.
• Identify false positives and false negatives before customers do, and clearly explain classification results and planned changes.
• Escalate complex issues to the R&D Data Analysts team with a complete, well-written handoff that can be acted on without a follow-up call.
• Track every request and its outcome, and use the data to identify volume, trends, and improvement opportunities.
• Review PoVs proactively to identify what is working, what is not, and what needs to change in classification, processes, or runbooks.
• Keep runbooks current by reporting gaps, proposing fixes, and updating instructions as the product evolves.