Data & Risk Strategy Enablement

WEX

. Serve as a trusted data and risk subject matter expert for data scientists, risk strategists, engineers, and business stakeholders .

Posted 11/12/2026full-timeRemote • United StatesMid-LevelSenior💰 $114,600 - $134,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data ecosystems, including data governance, quality, and lineage, while effectively translating business requirements into actionable data strategies. Proficient in optimizing data pipelines and feature stores to enhance analytical capabilities within financial services risk domains.

Highest-signal resume keywords

Advanced SQL SkillsData GovernanceFeature Store ExperienceData Quality PrinciplesCredit Risk Understanding

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringData AnalyticsETL/ELT PipelinesData TransformationsData CatalogingData LineageData ObservabilityFeature EngineeringCloud Data PlatformsCredit Risk Modeling

Soft Skills

Strong Communication SkillsRelationship BuildingProblem-Solving SkillsAnalytical SkillsInfluencing Without Authority

Tools & Technologies

Feature Engineering PlatformsData Quality PlatformsAI ToolsData WarehousesData Lakes

Industry Keywords

Financial ServicesRisk StrategyFraud RiskPaymentsData Science

Tech Stack

Tools & technologies

CloudETLSQL

About the role

Key responsibilities & impact

Serve as a trusted data and risk subject matter expert for data scientists, risk strategists, engineers, and business stakeholders
Develop deep understanding of critical source systems, data structures, business processes, and data flows supporting credit, fraud, and other risk strategies
Translate business and risk requirements into clear data requirements, identifying appropriate data sources, transformations, features, and analytical approaches
Establish and promote common definitions for risk, customer, account, transaction, and performance measures
Identify opportunities to improve data availability, usability, quality, and scalability
Bridge data consumers and data engineering teams by prioritizing and communicating data requirements and technical needs
Design, optimize, and improve pipelines supporting risk analytics, modeling, and strategy
Identify pipeline bottlenecks, redundancies, data quality issues, and efficiency and reliability improvements
Establish scalable data patterns that reduce duplicated data preparation
Participate in data architecture and pipeline change design and review
Identify upstream data changes and source-system dependencies affecting models, strategies, reporting, or monitoring
Build relationships with source-system owners and downstream data consumers
Serve as primary liaison between Risk Solutions and Strategy and the broader feature store ecosystem
Support feature-store adoption across data science, risk, and partner organizations
Establish best practices for feature development, documentation, reuse, governance, and consumption
Manage feature definitions, metadata, lineage, quality, ownership, and lifecycle management
Help migrate duplicated or manually maintained features into reusable, governed feature-store assets
Gather user feedback and translate it into feature-store enhancements and priorities
Promote feature reuse and reduce duplication across models and analytical solutions
Ensure features are discoverable, documented, trusted, and fit for production risk applications
Investigate and coordinate resolution of data issues across source systems, pipelines, and downstream consumers
Partner with data governance and technology teams to improve lineage, ownership, documentation, and controls
Identify gaps in data definitions and establish consistent business and technical definitions
Communicate data risks, dependencies, and limitations affecting analytical or risk outcomes
Build credibility and relationships across data engineering, technology, risk, data science, and source-system teams
Influence teams without direct authority and drive solutions across organizational boundaries
Identify reusable capabilities, common features, and scalable solutions

Requirements

What you’ll need

6 or more years of professional experience working with data, analytics, data science, risk, data engineering, or related disciplines
Preferably experience 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
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
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
Ability to simplify complex data environments and create reusable solutions that accelerate data science and risk strategy
Experience with AI tools such as Gemini, ChatGPT, or similar

Benefits

Comp & perks

Generous paid time off
Paid volunteering time
Health insurance
Dental insurance
Vision insurance
Retirement savings plan
Health savings account
Flexible spending accounts
Life insurance
Disability insurance
Tuition reimbursement
Quarterly or annual bonus eligibility for non-sales roles
Comprehensive and market competitive benefits
Diversity and inclusion culture