Data Analyst, Internal Audit
New
G
GiveDirectlyNonprofit Development
We can only employ people in certain places at the moment, so it's worth checking our current list of eligible countries to see if yours is included. If you're based elsewhere, it's not an automatic no, but do flag it early with your recruiter., Overlap with East Africa timezone by at least 3 hours.Full-TimeMiddle
SalaryUnited States: Base Salary $107,070; United Kingdom: Base Salary £70,250; Kenya: Base Salary $72,550.
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Job Details
- Experience
- 4+ years of experience in data analysis, audit, risk, or a related analytical role.
- Required Skills
- PythonSQLData AnalysisMicrosoft Power BISalesforceTableauData visualizationLooker
Requirements
- 4+ years of experience in data analysis, audit, risk, or related fields.
- Strong SQL skills with experience in large, complex operational datasets.
- Experience using Python for reproducible analysis, automation, or data quality testing.
- Ability to turn exploratory analysis into clear, defensible narratives for non-technical stakeholders.
- Familiarity with data visualization tools such as Looker, Tableau, or Power BI.
- Demonstrated ability to work cross-functionally with product and engineering teams.
- Sound judgment on evidence quality and ability to communicate uncertainty honestly.
- High integrity and discretion when handling sensitive investigative information.
- Alignment with organizational values regarding recipient wellbeing.
- Experience with version control and analytical notebooks is a plus.
Responsibilities
- Mine enrollment, payment, survey, and field-operations data to identify fraud patterns.
- Build, validate, and maintain recurring fraud-risk monitoring in partnership with stakeholders.
- Identify and resolve data quality issues within Internal Audit data.
- Embed data-driven decision-making processes into Internal Audit design.
- Package analytical findings into clear, actionable briefs for leadership.
- Provide data pulls and quantify exposure for fraud investigations while ensuring reproducibility.
- Contribute domain expertise and validate product requirements for fraud-detection tools.
- Collaborate with Data Engineering on upstream data quality and pipeline improvements.
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