Data & Risk Strategy Enablement

Data & Risk Enablement

• Serve as a trusted data and risk subject matter expert for data scientists, risk strategists, engineers, and business stakeholders.
• Develop a deep understanding of critical source systems, data structures, business processes, and data flows that support credit, fraud, and other risk strategies.
• Translate business and risk requirements into clear data requirements, helping teams identify the right data sources, transformations, features, and analytical approaches.
• Establish and promote common definitions for key risk, customer, account, transaction, and performance measures to improve consistency across analytics and decisioning.
• Identify opportunities to improve the availability, usability, quality, and scalability of data used by risk and data science teams.
• Act as a bridge between data consumers and data engineering teams, helping prioritize and communicate data requirements and technical needs.

Data Pipeline Optimization

• Partner closely with data engineering teams to design, optimize, and improve pipelines supporting risk analytics, modeling, and strategy.
• Identify bottlenecks, redundancies, data quality issues, and opportunities to improve the efficiency and reliability of existing data pipelines.
• Help establish scalable data patterns that reduce duplicated data preparation and enable data scientists and risk strategists to focus on higher-value analytical work.
• Participate in the design and review of data architecture and pipeline changes to ensure they meet downstream analytical and risk requirements.
• Proactively identify upstream data changes or source-system dependencies that could impact models, strategies, reporting, or monitoring.
• Develop strong relationships with source-system owners and downstream data consumers to improve transparency and accountability across the data ecosystem.

Feature Store Optimization

• Translate and support the relationship between the Risk Solutions and Strategy organization and the broader feature store ecosystem.
• Serve as the primary liaison to external teams supporting or consuming the feature store, representing the needs of data science and risk strategy.
• Support the feature store across data science, risk, and partner organizations by identifying barriers and creating practical solutions.
• Establish and promote best practices for feature development, documentation, reuse, governance, and consumption.
• Partner with teams to retain and monitor feature definitions, metadata, lineage, quality, ownership, and lifecycle management.
• Work with data scientists and engineers to identify opportunities to migrate duplicated or manually maintained features into reusable, governed feature-store assets.
• Gather feedback from internal users and translate it into enhancements, capabilities, and priorities for the feature store.
• Promote reuse of existing features and reduce unnecessary duplication across models and analytical solutions.
• Help ensure that features are discoverable, well documented, trusted, and fit for use in production risk applications.

Data Quality & Governance

• Establish a strong quality mindset around data used for risk decisioning and model development.
• Investigate and coordinate resolution of data issues across source systems, pipelines, and downstream consumers.
• Partner with data governance and technology teams to improve data lineage, ownership, documentation, and controls.
• Identify gaps in data definitions and work across teams to establish consistent business and technical definitions.
• Proactively communicate data risks, dependencies, and limitations that could affect analytical or risk outcomes.

How You’ll Engage

• Trusted Partner: Build credibility through deep knowledge of data, risk, source systems, and the needs of analytical users.
• Data Translator: Connect business and risk objectives with technical data requirements and communicate effectively across technical and non-technical audiences.
• Enablement Mindset: Make it easier for data scientists, risk strategists, and partner teams to find, understand, trust, and use data.
• Enterprise Collaboration: Build strong relationships across data engineering, technology, risk, data science, and source-system teams to solve problems that cross organizational boundaries.
• Quality Mindset: Treat data quality, lineage, definitions, and reliability as foundational to trusted risk decisions.
• Scalable Thinking: Look beyond individual data requests to identify reusable capabilities, common features, and scalable solutions.
• Continuous Improvement: Challenge inefficient processes and identify opportunities to improve how data is created, managed, and consumed.
• Curiosity & Learning: Develop a deep understanding of evolving data platforms, feature stores, source systems, and analytical technologies and share that knowledge with others.

Experience You’ll Bring

• 6 or more years of professional experience working with data, analytics, data science, risk, data engineering, or related disciplines, preferably within financial services or a complex data environment.
• Strong understanding of data ecosystems, including source systems, data warehouses/lakes, ETL/ELT pipelines, data transformations, and downstream analytical consumption.
• Advanced SQL skills and experience working with large, complex datasets.
• Demonstrated ability to understand complex source systems and translate business processes into data requirements and analytical datasets.
• Experience partnering with data engineering teams to build, optimize, and troubleshoot data pipelines.
• Strong understanding of data quality principles, data lineage, metadata, data governance, and data ownership.
• Experience working with feature stores, feature engineering platforms, or similar reusable data/analytics infrastructure.
• Ability to understand data science and machine learning requirements, including how features are developed, evaluated, consumed, and operationalized.
• Strong understanding of credit risk, fraud risk, payments, or other financial services risk domains.
• Strong communication and relationship-building skills, with the ability to influence teams without direct authority.
• Demonstrated ability to operate independently, identify ambiguous problems, and drive solutions across organizational boundaries.
• Strong analytical, problem-solving, and critical-thinking skills.
• Experience serving as a data or analytics subject matter expert across multiple teams or organizational functions.
• Experience owning or helping establish a feature store, feature platform, or enterprise analytics data product.
• Experience partnering on shared data platforms or capabilities across data science and engineering organizations.
• Experience developing enterprise data definitions, data standards, or data governance practices.
• Experience working with cloud data platforms and modern data architectures.
• Experience with data cataloging, lineage, data observability, or data quality platforms.
• Experience working in an environment where data science, risk strategy, and data engineering operate as highly interconnected functions.
• Experience with credit risk modeling, portfolio management, underwriting, fraud, collections, or other financial risk applications.
• Experience influencing technical roadmaps and priorities across teams without direct management authority.
• Demonstrated ability to simplify complex data environments and create reusable solutions that accelerate data science and risk strategy.
• AI -- Gemini, ChatGPT or Similar
• The base pay range represents the anticipated low and high end of the pay range for this position.
• Actual pay rates will vary and will be based on various factors, such as your qualifications, skills, competencies, and proficiency for the role.
• Base pay is one component of WEX's total compensation package.
• Most sales positions are eligible for commission under the terms of an applicable plan.
• Non-sales roles are typically eligible for a quarterly or annual bonus based on their role and applicable plan.
• WEX's comprehensive and market competitive benefits are designed to support your personal and professional well-being.
• Benefits include health, dental and vision insurances, retirement savings plan, paid time off, health savings account, flexible spending accounts, life insurance, disability insurance, tuition reimbursement, and more.