Evaluate existing data integrations for opportunities to reduce latency and implement necessary changes
Partner with central Data and platform teams on warehouse connections, schema changes, API behavior, and source-system migrations
Support integrations between recruiting systems and internal tools including writeback of data updates
Design data access and reporting solutions that respect confidentiality, role-based permissions, and regional or organizational access boundaries
Create clear documentation for sensitive workflows and ensure that operational users understand the limitations of the data
Proactively identify opportunities to improve reporting and workflows as recruiting processes change
Partner with team members, engineering, product and business leaders to understand current user behavior and patterns across our platform
- Monitor pipelines for stability, suggest and implement efficiency initiatives
- What We Value
- Proven ability to build and maintain data pipelines, datasets, and analytics data products
Hands-on experience with Foundry, Contour, or comparable data and analytics platforms
Background working with Recruiting, HR, People, CRM, ATS, or other operational data; Lever experience preferred
Proficiency integrating APIs, data warehouses, and SaaS systems
Familiarity with workflow automation, writeback processes, and event-driven integrations
- Working knowledge of data governance, privacy, retention, access controls, and compliance-related workflows
- What We Require
- Degree in Data Science, Finance, Math, Engineering, Computer Science, Economics, Business or other quantitative fields
1+ years of experience in a data analytical role
Expertise in Python and data manipulation to answer complex questions. Experience with pySpark, pandas, or polars is a plus.
Strong SQL skills and experience working with relational or structured data.
Proficiency with BI tools, SQL, and the organization and analysis of data
Fluency in TypeScript, or another general-purpose programming language
Ability to create reusable data models, testing frameworks, and data quality checks
