First VP Data and Analytics at PennyMac Financial Services in Carrollton, TX

Job description

PENNYMAC Pennymac is (NYSE: PFSI) is a specialty financial services firm with a comprehensive mortgage platform and integrated business focused on the production and servicing of U.S. mortgage loans and the management of investments related to the U.S. mortgage market. At Pennymac, our people are the foundation of our success and at the heart of our dynamic work culture. Together, we work towards a unified goal of helping millions of Americans achieve aspirations of homeownership through the complete mortgage journey. A Typical Day This role leads the Production Finance data platform that designs, builds, and operates enterprise Data Mart / Hub structures used to measure operational and regulatory risk, support decision-making, and enable downstream reporting and analytics. Owns the end-to-end lifecycle of trusted production data: ingestion and transformation standards, platform migration and modernization (legacy SQL Server → Snowflake), data quality and validation, field-level logic/lineage, and partner delivery of contractual and corporate data commitments. Champions modern engineering practices, including AI-assisted development with Cursor, to accelerate delivery while preserving accuracy, governance, and production controls. Partners with IT, operations, finance, and investor/business stakeholders to ensure data is accurate, timely, well-governed, and fit for use — without owning the downstream BI/reporting layer itself. The First Vice President, Data & Analytics will: Data Mart / Hub Platform Leadership Own strategy and delivery for Production Finance Data Mart / Hub assets (tables, stored procedures, pipelines) that store and transform enterprise information into definitive, consumption-ready structures. Set engineering standards for sequential, high-performance SQL (legacy T-SQL and Snowflake SQL), deployment discipline, and operational reliability. Partner with IT on storage, transformation, security, and change-control protocols across on-prem SQL Server and Snowflake. Drive modernization of legacy SQL Server data platforms to Snowflake, with zero-tolerance standards for timing, syntax, and data-type mismatches during cutover. AI-Enabled Engineering (Cursor) Establish and scale team standards for AI-assisted development using Cursor (and related AI coding tools) for SQL authoring, migration, validation, lineage analysis, and operational triage. Define guardrails so AI acceleration does not compromise data correctness, security, change control, or production deploy discipline. Coach engineers on effective human-in-the-loop use of Cursor: prompt discipline, review of AI-generated SQL/scripts, and accountability for outcomes. Continuously evaluate opportunities to improve throughput and consistency through AI-augmented workflows without shifting ownership of production decisions away from the team. Data Quality, Validation & Governance Lead the Data Governance framework for the division: identify, design, implement, and support data quality improvement initiatives using manual and automated controls. Establish continuous monitoring to detect, correct, and prevent invalid data; own issue tracking, remediation with business and IT partners, and quality metrics tied to organizational standards. Direct validation and triage practices (schema alignment, completeness, cell-level reconciliation) so production hubs remain trusted sources for risk, ops, and finance outcomes. Ensure field-level business logic and lineage are documented and explainable to downstream consumers and control partners. Analytics Enablement & Executive Insight (not report ownership) Enable Executive Reporting and Analytics by ensuring hubs, metrics definitions, and data contracts support Monthly Executive Management Reporting, business reviews, and recurring daily/weekly/monthly management metrics. Provide leadership and prioritization so operations and finance leaders receive accurate, timely data for planning, efficiency, controls, and client experie