Hybrid Process Manager (Associate)

Hybrid Process Manager (Associate) The Associate, Data Process Manager will be a critical driver of operational efficiency, quality control, and data integrity across our Quality Assurance (QA) monitoring teams and operational support towers. This role bridges the gap between technical execution and business operations, combining hands-on data management—such as executing SQL scripts through Databricks and Argos, maintaining code repositories in GitHub, and building dynamic dashboards—with structured process management and continuous operational improvement. The ideal candidate possesses strong analytical rigor, meticulous attention to detail, and proactive communication skills. Functioning with minimal day-to-day oversight, they will influence operational outcomes, support audit and compliance requirements, and ensure that QA leadership has timely, reliable, and actionable insights to maintain enterprise quality standards.

1. Data Reporting & Dashboard Management Dashboard Ownership: Design, build, and maintain operational dashboards and automated reporting suites (e.g., Tableau, Snowflake) used by QA Managers, Operations Leaders, and Executive Stakeholders to track key quality metrics and performance trends. Ad-Hoc Analysis: Fulfill ad-hoc data extraction and reporting requests across various partner groups, including Operations, Process Improvement, Project Management, and Internal Audit. 2. Databricks, Argos & GitHub Code Governance Script Execution & Operations: Run, schedule, and monitor routine data production jobs and SQL scripts executed through both Databricks notebooks and Argos , ensuring timely and accurate data delivery with zero disruption to QA workflows. GitHub Repository Management: Maintain version control across all team SQL queries, Databricks notebooks, scripts, and automation workflows in GitHub , ensuring clean repository architecture and code documentation. Script Governance & Validation: Lead the team’s Script Governance process by managing pull requests, conducting dual control/peer code reviews in GitHub, and performing output testing prior to executing scripts in production. 3. Process Management & Continuous Improvement Process Enhancement: Continuously evaluate current QA data collection, reporting, and operational workflows to identify bottlenecks, redundancies, and opportunities for automation or refinement. Operational Partnering: Engage closely with frontline Operations partners (tracked via JIRA) to capture user experience feedback, address data pain points, and streamline workflow handoffs between QA and Ops. Data Registration & Risk Compliance: Manage data process registration needs, data lineage documentation, and compliance artifacts to adhere to corporate data governance frameworks. 4. Testing & Data Integration User Acceptance Testing (UAT): Lead UAT efforts for new operational data sources, database migrations, and schema changes to ensure data reliability prior to production deployment. The ideal candidate will have A proficiency in using SQL to write complex joins, CTEs, aggregations, window functions, and optimizing queries Hands-on experience executing scripts via Databricks and Argos, alongside code repository management using GitHub (or Git version control) Experience building dashboards in Tableau (or similar BI tools) and querying cloud data warehouses like Snowflake Familiar with using JIRA and Confluence for logging tickets, tracking operational enhancements, and documenting process workflows. Experience using either Python or PySpark for advanced data processing within Databricks Location: This role is hybrid, where you will be expected to spend 3 days per week working in office and the remainder of the week working virtually.

High School Diploma, GED or equivalent certification At least 1 years of of experience in data operations, QA reporting, operational analytics, or process improvement At least 1 year experience using