Sr Data Steward and Analyst

Worth AI, a forward‑thinking company in the computer software industry, is looking for a detail‑oriented and analytical individual to join their team as a Senior Data Steward. At Worth AI, we strive to drive impactful decision‑making through insights derived from data, while upholding values of one team, extreme ownership and creating raving fans both internally and for our customers. As a Senior Data Steward within our fintech organization, you will be the owner and advocate for data integrity, definition, and trustworthiness across the company. You will ensure that every data element used in underwriting, credit decisioning, and fraud prevention is well‑defined, accurate, and reliable. Acting as the face of the business for data meaning and quality, you'll collaborate with Finance, Operations, Product, and Engineering teams to document data lineage, define key metrics, and monitor the health of our data ecosystem. Beyond governance, you'll mine large datasets, build data‑quality dashboards, and deliver actionable insights that shape our AI‑driven underwriting strategy. This role requires both technical proficiency and business acumen — ideal for someone who loves data, details, and cross‑functional communication. Responsibilities Define and maintain data meaning: Serve as the primary owner of the data dictionary and business glossary, ensuring every field, metric, and table has clear definitions, lineage, and ownership Monitor and improve data quality: Develop and maintain dashboards that measure completeness, accuracy, timeliness, and consistency of key datasets across the data ecosystem Establish data standards: Partner with Engineering and Product teams to implement consistent naming conventions, validation rules, and quality thresholds Document lineage and dependencies: Trace how data flows through pipelines and models, from origination to credit decisioning and risk reporting Be the face of data for the business: Act as the go‑to expert for interpreting data fields, business rules, and metrics enabling cross‑functional alignment on how data is used and reported Champion data integrity: Lead initiatives to identify, escape, and resolve data discrepancies or anomalies before they impact analytics or model outputs Mine and analyze large‑scale credit, transactional, and behavioral datasets to identify trends, anomalies, and insights that inform credit risk, underwriting, fraud, and pricing strategies Write performant SQL queries to extract, transform, and analyze data across structured and semi‑structured sources (including loan‑level, customer, and bureau data) Design, build, and maintain interactive dashboards and reporting tools to support Finance, Risk, Operations, and Executive teams in monitoring key performance indicators Partner with Operations, Finance, Product, and Engineering teams to define and track key metrics Support AI/ML model development through exploratory data analysis, feature validation, and input quality assurance Automate recurring reports and analyses to improve visibility and reduce manual workflows Collaborate with Data Engineering to ensure pipelines are reliable, secure, and aligned with data governance standards Contribute to model governance by ensuring documentation, auditability, and transparency of data inputs used in decision engines Requirements Bachelor's degree in Data Science, Statistics, Finance, Computer Science, or a related technical field 4+ years of experience in a data analytics or business intelligence role, ideally in a fintech, financial services, or credit risk environment Advanced SQL skills and experience working with cloud data platforms (e.g., AWS Redshift, Athena, Snowflake, BigQuery) Strong experience with data visualization tools like Tableau, Power BI, or Looker, and the ability to build intuitive dashboards for business stakeholders Solid understanding of credit risk concepts, underwriting processes, and key lending KPIs such as delinquency, charge‑off rates, loss curves, and approval funnels Exposure to AI/ML‑based decision systems, especially in underwriting, fraud detection, or credit scoring contexts Working knowledge of data engineering fundamentals and modern data stack tools (e.g., dbt, Airflow, ETL pipelines) Proficiency in Excel for detailed analysis and cross‑functional reporting Bonus: Experience with Python or other scripting languages for data analysis and automation Excellent communication and problem‑solving skills, with the ability to present complex data insights to technical and non‑technical stakeholders alike High standards for data integrity, governance, and regulatory compliance in a financial environment Benefits Health Care Plan (Medical, Dental & Vision) Retirement Plan (401k, IRA) Life Insurance Unlimited Paid Time Off 9 paid Holidays Family Leave Work From Home Free Food & Snacks (Access to Industrious Co‑working Membership!) Wellness Resources #J-18808-Ljbffr Worth AI