Sr Data Steward and Analyst

Worth AI

Tampa (FL)

On-site

USD 85,000 - 115,000

Full time

14 days+

Get more replies from employers

Send a job-specific resume in minutes.

Benefits offered by this job

Health Care Plan (Medical, Dental & Vision)

Retirement Plan (401k, IRA)

Unlimited Paid Time Off

Work From Home

Free Food & Snacks

Job summary

A forward-thinking fintech company is seeking a Senior Data Steward to ensure data integrity and oversee data quality across teams. The ideal candidate will have advanced SQL skills and experience in analytics or business intelligence, plus a solid understanding of credit risk concepts. This role offers a collaborative environment with opportunities for significant impact on data-driven strategies.

Qualifications

4+ years of experience in a data analytics or business intelligence role.
Solid understanding of underwriting processes and key lending KPIs.
Experience with cloud data platforms and data visualization tools.

Responsibilities

Serve as the primary owner of the data dictionary and business glossary.
Develop dashboards that measure data quality across datasets.
Trace how data flows through pipelines and models.

Skills

Advanced SQL skills

Data visualization tools (Tableau, Power BI)

Excellent communication skills

Problem-solving skills

Knowledge of credit risk concepts

Working knowledge of data engineering fundamentals

Education

Bachelor's degree in Data Science or related field

Tools

AWS Redshift

Tableau

Python

Power BI

Job description

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