Deep expertise in SQL against large production datasets, including traditional relational database management systems such as SQL Server and T-SQL, and Snowflake.
Proven experience owning or leading enterprise data mart, hub, or warehouse engineering, including extract, transform, load or extract, load, transform processes, stored procedures, and pipeline reliability; report development alone is insufficient.
Experience adopting and leading teams in AI-assisted engineering workflows, preferably Cursor, while enforcing rigorous review of AI-generated code and SQL.
Strong understanding of financial and operational drivers in financial services.
Hands-on fluency with data quality, reconciliation, and validation methods at scale, including completeness, accuracy, and schema or contract drift.
Experience with platform migration and dual-running environments using legacy SQL Server and Snowflake, including timing windows, type alignment, and cutover risk management.
Familiarity with continuous integration and continuous delivery for data, such as Git-based change management and Liquibase or equivalent, and structured collaboration with IT release processes.
Ability to decode and communicate field-level business logic and lineage to executives, auditors, and downstream analytics consumers.
Ability to prioritize multiple concurrent initiatives in a dynamic production environment.
Proficiency with Excel and presentation tools for stakeholder communication; advanced spreadsheet work is supporting rather than the core craft.
Track record of delivering timely, accurate information under operational pressure.
Ability to interface with executive management, IT, and control partners.
Ability to lead engineers while remaining technically credible, with high attention to detail.
Responsibilities
Own strategy and delivery for Production Finance Data Mart and Hub assets, including tables, stored procedures, and pipelines that store and transform enterprise information into definitive, consumption-ready structures.
Set engineering standards for sequential, high-performance SQL using legacy T-SQL and Snowflake SQL, deployment discipline, and operational reliability.
Partner with IT on storage, transformation, security, and change-control protocols across on-premises SQL Server and Snowflake.
Drive modernization of legacy SQL Server data platforms to Snowflake, ensuring timing, syntax, and data-type alignment during cutover.
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-assisted development preserves data correctness, security, change control, and production deployment discipline.
Coach engineers on human-in-the-loop use of Cursor, including prompt discipline, review of AI-generated SQL and scripts, and accountability for outcomes.
Evaluate opportunities to improve throughput and consistency through AI-augmented workflows while retaining team ownership of production decisions.
Lead the division’s data governance framework by identifying, designing, implementing, and supporting 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, including schema alignment, completeness, and cell-level reconciliation, so production hubs remain trusted sources for risk, operations, and finance outcomes.
Ensure field-level business logic and lineage are documented and explainable to downstream consumers and control partners.
Enable executive reporting and analytics by ensuring hubs, metric definitions, and data contracts support monthly executive management reporting, business reviews, and recurring daily, weekly, and monthly management metrics.
Provide leadership and prioritization so operations and finance leaders receive accurate, timely data for planning, efficiency, controls, and client experience; coordinate with reporting and analytics partners rather than building dashboards or presentation layers as the team’s core product.
Evaluate and implement strategies for using data and metrics to predict, improve, and measure fundamental risks to the division and operation.
Oversee development of operational capacity and forecast inputs, including volumes and staffing drivers, insofar as they depend on hub data products, metric definitions, and reliable historical and current-state data.
Lead key corporate and financially material data initiatives affecting Production Finance.
Oversee investor and business-partner contractual obligations related to data and reporting delivery, including definitions, timeliness, reconciliation, and change management.
Align internal and external parties on data handoffs and process integrity for financially impacting workflows that depend on hub outputs.
Perform other related duties as required and assigned.
Desired Qualifications
Mortgage banking, servicing, or production finance experience is strongly preferred.
Experience with Cursor is preferred.
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.
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 experience — coordinating with reporting/analytics partners rather than building dashboards or presentation layers as the team’s core product.
Evaluate and implement strategies for using data and metrics to predict, improve, and measure fundamental risks to the division/operation.
Operational Modeling & Capacity Support
Oversee development of operational capacity and forecast inputs (volumes, staffing drivers, etc.) insofar as they depend on hub data products, metric definitions, and reliable historical/current-state data.
Lead delivery of key corporate and financially material data initiatives affecting Production Finance.
Manage oversight of Investor and Business Partner contractual obligations related to data and reporting delivery (definitions, timeliness, reconciliation, change management).
Align internal and external parties on data handoffs and process integrity for financially impacting workflows that depend on hub outputs.
Perform other related duties as required and assigned.
Demonstrate behaviors aligned with the organization’s desired culture and values.
What You’ll Bring
Deep expertise in SQL against large production datasets; strong comfort with both traditional RDBMS (e.g., SQL Server / T-SQL) and Snowflake.
Proven experience owning or leading enterprise data mart / hub / warehouse engineering (ETL/ELT, stored procedures, pipeline reliability) — not solely report development.
Experience adopting and leading teams in AI-assisted engineering workflows (Cursor preferred); able to raise productivity while enforcing rigorous review of AI-generated code and SQL.
Strong understanding of financial and operational drivers in financial services; mortgage banking / servicing / production finance experience strongly preferred.
Hands-on fluency with data quality, reconciliation, and validation methods at scale (completeness, accuracy, schema/contract drift).
Experience with platform migration and dual-running environments (legacy SQL Server + Snowflake), including timing windows, type alignment, and cutover risk management.
Familiarity with CI/CD for data (e.g., Git-based change management, Liquibase or equivalent) and structured collaboration with IT release processes.
Ability to decode and communicate field-level business logic and lineage to executives, auditors, and downstream analytics consumers.
Strong project leadership: prioritize multiple concurrent initiatives in a dynamic production environment.
Self-directed; operates with urgency and accountability in a fast-paced business setting.
Excellent critical thinking, problem solving, quantitative judgment, and executive-ready verbal/written communication.
Proficient with Excel and presentation tools for stakeholder communication; advanced spreadsheet work is supporting, not the core craft.
Track record of delivering timely, accurate information under operational pressure.
Strong business acumen and ability to interface with executive management, IT, and control partners.
Team builder with high attention to detail; able to lead engineers while remaining technically credible.
WHAT SUCCESS LOOKS LIKE
Production hubs are the trusted source of truth for division risk and performance metrics.
Migrations and changes ship with measurable zero-defect (or rapidly remediated) validation outcomes.
Governance and quality issues are visible, owned, and closed with clear metrics.
Downstream reporting and ops partners can explain “why this field is what it is” without tribal knowledge.
Contractual and corporate data commitments are met on time with auditable controls.
The team consistently uses Cursor / AI-assisted workflows to move faster without sacrificing accuracy, auditability, or production control.
Why You Should Join
As one of the top mortgage lenders in the country, Pennymac has helped over 4 million lifetime homeowners achieve and sustain their aspirations of home. Our vision is to be the most trusted partner for home. Together, 4,000 Pennymac team members across the country are guided by our core values: to be Accountable, Reliable and Ethical in all that we do.
Pennymac is committed to conducting a business that makes positive contributions and promotes long-term sustainable growth and to fostering an equitable and inclusive environment, where all employees and customers feel valued, respected and supported.
Benefits That Bring It Home: Whether you're looking for flexible benefits for today, setting up short-term goals for tomorrow, or planning for long-term success and retirement, Pennymac's benefits have you covered. Some key benefits include:
Comprehensive Medical, Dental, and Vision
Paid Time Off Programs including vacation, holidays, illness, and parental leave
Wellness Programs, Employee Recognition Programs, and onsite gyms and cafe style dining (select locations)
Retirement benefits, life insurance, 401k match, and tuition reimbursement
Philanthropy Programs including matching gifts, volunteer grants, charitable grants and corporate sponsorships
We value the hard work and dedication of our employees. In addition to a competitive salary, positions may offer bonus opportunities.
Compensation: Individual salary may vary based on multiple factors including specific role, geographic location / market data, and skills and experience as defined below:
Lower in range - Building skills and experience in the role
Mid-range - Experience and skills align with proficiency in the role
Higher in range - Experience and skills add value above typical requirements of the role
Some roles may be eligible for performance-based compensation and/or stock-based incentives awarded to employees based on company and individual performance.