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Data Science & Analytics 1h ago
Analytics Engineer
United StatesFull-time
$138,750 - $161,250 per year
Mid-Level
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Job Description
Key Skills Required
Master these to land this role
Data AnalystBestseller 🔥
Learn in 88 HoursSQLBestseller 🔥
Learn in 9 HoursdbtData ScientistSnowflakeBusiness IntelligenceBig DataData Scraping
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About the Role:
We're looking for an Analytics Engineer to help ensure data integrity across Extend's financial and operational systems. This architecture powers reconciliation, audit readiness, and payment accuracy for the Servicers and Merchants we work with. This is a cross-functional role. You'll partner with Accounting, Operations, Reporting, and Engineering to align internal and external financial data, and build well-documented, reliable pipelines in Snowflake and dbt. It's a hands-on role anchored in SQL and analytics engineering, with real ownership over the reconciliation workflows you support.
Key Responsibilities:
- Data Pipeline Development. You build and maintain data pipelines in Snowflake and dbt to support reliable, auditable reconciliation between internal and external systems.
- Claim Reimbursement & Contract Sales Ownership. You own and improve the data workflows related to Claim Reimbursement and Contract Sales, ensuring accurate financial tracking and reconciliation with partners.
- Cross-Functional Partnership. You work closely with Accounting, Engineering, Operations, and Reporting to align and reconcile internal and external financial data, acting as a connective link between technical and non-technical teams.
- Data Quality & Audit Readiness. You ensure data integrity across systems and support internal and external audits through well-documented, traceable data practices.
- Integrations & APIs. You use tools like Thunderclient and Postman to interface with production systems, and help enhance integrations with Service Providers and Merchants to improve the timeliness and accuracy of claim expense reporting and payments.
- Issue Investigation & Process Improvement. You identify, investigate, and fix data quality issues, recommending process improvements to reduce errors and manual intervention, and help extend our data quality standards and operational analytics practices.
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