Data Analyst II

Qualifications

• Education : Bachelor's degree preferred; not required. We evaluate demonstrated capability rather than credentials.
• Licensure/Certification : Not applicable.
• Experience : Typically 2 to 4 years of experience in data analysis. This band is guidance rather than a gate.
• Knowledge, Skills, and Abilities : Can take a vague question, sharpen it into something answerable, and come back with the answer and its caveats without needing to be checked on daily. Validates work: counts rows, compares against a second source, and asks why a total does not tie before anyone else has to ask. Writes and speaks plainly about what you found, so someone in an account or operations role can read your summary and act on it. Comfortable being told a number is wrong: finds out why and says so rather than quietly patching it, and is curious about how the healthcare and pharmacy operation actually works. A couple of years of analysis work where SQL was the main tool: you can write more than a simple join, including window functions and careful aggregation, and you take the time to figure out grain before writing anything. Demonstrated application of the above Qualification Nice to have: pharmacy, claims, or payer data experience (prior authorizations, adjudication, benefit verification, NDCs, dispensing workflows); dbt experience contributing transformation logic rather than only reading from it; Looker or LookML exposure; Snowflake (we are mid-migration from PostgreSQL); Python for analysis that does not fit in SQL; or experience producing work that went to an external client.
• Can take a vague question, sharpen it into something answerable, and come back with the answer and its caveats without needing to be checked on daily.
• Validates work: counts rows, compares against a second source, and asks why a total does not tie before anyone else has to ask.
• Writes and speaks plainly about what you found, so someone in an account or operations role can read your summary and act on it.
• Comfortable being told a number is wrong: finds out why and says so rather than quietly patching it, and is curious about how the healthcare and pharmacy operation actually works.
• A couple of years of analysis work where SQL was the main tool: you can write more than a simple join, including window functions and careful aggregation, and you take the time to figure out grain before writing anything.
• Demonstrated application of the above Qualification
• Nice to have: pharmacy, claims, or payer data experience (prior authorizations, adjudication, benefit verification, NDCs, dispensing workflows); dbt experience contributing transformation logic rather than only reading from it; Looker or LookML exposure; Snowflake (we are mid-migration from PostgreSQL); Python for analysis that does not fit in SQL; or experience producing work that went to an external client.

Work Environment

Key Essential Functions

Employment Classification

Equal Employment Opportunity (EEO) Statement

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