Vice President of Engineering, Data

Convera•San Francisco, CA

3d•$350,000 - $400,000•Hybrid

About The Position

Convera is a leading B2B cross-border payments company. This role owns Data Engineering, Solution Data Architecture, and Data Science, and is accountable for delivering Convera's enterprise data platform while ensuring continuity of existing operational and regulatory workloads throughout the transformation. The primary responsibility is modernizing the enterprise data estate by progressively migrating legacy reporting platforms to the Convera Enterprise Data Platform (CEDP), enabling trusted reporting, analytics, AI and regulatory capabilities on a unified foundation. The initial focus is on taking hold of an in-flight transformation without disruption, completing it, and establishing a data estate that satisfies regulatory and auditing requirements and supports enterprise AI.

Requirements

15+ years in data, including 8+ years in management and the senior-most data role (VP Data, Head of Data, or CDO) at a fintech, payments, or regulated financial services company.
Proven track record of delivering a consolidated data platform end-to-end, from a fragmented legacy estate to a single governed store.
Modern cloud data warehouse experience (Snowflake, Databricks, or comparable).
Deep data engineering credentials: large-scale ETL and ELT, pipeline reliability, reconciliation, and performance in challenging environments.
Experience implementing and sustaining data governance in practice (lineage, definitions, stewardship, and quality).
In-depth knowledge of data privacy and secure data handling: encryption, masking, tokenization, access control, and retention obligations across multiple jurisdictions.
Direct experience interacting with regulators, examiners, and auditors.
Leadership experience spanning data engineering, architecture, and data science, including production machine learning that influenced real business decisions.
Hands-on experience preparing data for ML: labeling, feature engineering, and creating training-ready assets at scale.
A transformation track record: successfully taken over an existing organization, assessed talent and technology, and improved accountability without stalling committed delivery.
Bachelor's degree required.

Nice To Haves

Cross-border payments, FX, or treasury data experience.
Experience in a PE-backed environment.

Responsibilities

Deliver the single data store, consolidating Convera's data into one governed platform for reporting, analytics, and AI enablement.
Lead the phased migration from legacy reporting platforms to the enterprise data platform while maintaining uninterrupted business and regulatory operations.
Manage the committed roadmap for an in-flight program, ensuring continuity and sustained delivery.
Run the operational data store (ODS), ensuring its stability and feeding transactional systems during migration, and determine its future state post-migration.
Partner with Enterprise Architecture on enterprise-wide data standards, canonical models, reference architecture, information models, and technology strategy, defining operating boundaries.
Build scalable ETL and ELT pipelines, including streaming pipelines for real-time data movement, with robust reconciliation, monitoring, and recovery.
Own data science initiatives, setting direction for analytical and machine learning work impacting pricing, margin, risk, and client behavior, and partnering on model governance.
Own data governance, quality, and stewardship, establishing definitions, lineage, ownership, and quality service levels that the business trusts.
Implement data protection by design, including secure storage, encryption, masking, tokenization, access control, and retention across all jurisdictions.
Represent Convera to regulators and auditors, explaining the data estate and answering questions.
Serve the business by providing governed, documented, well-modeled data to teams responsible for dashboards and reporting, and build a searchable data catalog for self-serve discovery.
Architect for future data products, including democratized data APIs for external data surfacing.
Lead the data organization by assessing talent and technology, reshaping the team, and setting an operating cadence that balances delivery and transformation. Partner with Product, Engineering, Operations, Finance, Risk, and Compliance to align data priorities with enterprise outcomes.