Senior Manager, Data Strategy & Analytics

Happy Money · USA · $145k – $160k · posted today ago

python data remote jobs in united states

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About this role

ABOUT HAPPY MONEY

Happy Money is a consumer finance company that empowers people to achieve their goals through simple, transparent loans funded by trusted financial institutions. Through its proprietary lending platform, Hive, Happy Money delivers a fully digital experience that simplifies borrowing for consumers and helps credit unions, banks and asset managers diversify balance sheets and scale with confidence.

Happy Money’s end-to-end loan origination and participation programs provide partners with turnkey access to high-performing assets, underpinned by disciplined credit expertise and robust risk management. Together with its lending partners, Happy Money has originated more than $6.5 billion in loans to help nearly 350,000 people take greater control of their financial futures. Learn more at happymoney.com. View our Employee Privacy Policy here.

Happy Money is a 100% fully distributed company. Remote work doesn’t mean less work, we work full-time jobs and show up to them fully every day. To learn more about our values and the way we work, review our Happy Money Way Overview!

ABOUT THE ROLE

Happy Money is looking for a Senior Manager, Data Strategy & Analytics to join our Risk/Credit team. In this role, you'll serve as a strategic analytics partner across the organization, translating complex data into actionable insights that drive decision-making across Credit, Operations, Marketing, Product, and Finance. You'll work with large datasets in a modern cloud-based environment, building dashboards, developing analytical models, and uncovering trends that help us deliver on our mission of helping people achieve financial wellness through responsible personal lending.

RESPONSIBILITIES

Develop and automate analytical workflows using Python and/or R, with code managed through GitHub
Design, build, and maintain dashboards and reports in Tableau that provide visibility into key business metrics across Credit, Operations, Marketing, and Finance
Partner with Data Engineering to build and refine data work flows in Databricks that support reporting and analytics needs
Conduct ad hoc analyses to answer strategic business questions, presenting findings with clear recommendations to leadership
Monitor data quality and proactively identify anomalies or issues in key metrics
Develop and document analytical methodologies, continuously evaluating opportunities to optimize processes and introduce improved approaches that enhance efficiency and scalability

ABOUT YOU

You are an analytical professional who excels at transforming complex data into meaningful business insights. You combine technical expertise with strong communication skills and collaborate effectively across functions to deliver high-quality work. You bring intellectual curiosity to every project and maintain a commitment to accuracy and precision in your analyses.

7+ years of experience in business intelligence, data analytics, or a related field, preferably within financial services or FinTech
Strong SQL skills with experience querying cloud data warehouses such as Snowflake
Proficiency in Tableau or similar visualization tools, with a portfolio of dashboards you've designed for business users
Working knowledge of Python and/or R for data manipulation, analysis, and automation
Familiarity with Databricks, Spark, or similar distributed computing platforms
Experience using Git/GitHub for version control and collaborative development
Excellent communication skills with the ability to distill complex analyses into clear narratives for diverse audiences
Comfort operating in a fast-paced environment where priorities shift and ambiguity is common

BONUS POINTS FOR

Experience in consumer lending, credit risk modeling, or the personal loan industry
Passion for leveraging AI tools and generative AI to enhance analytics workflows, automate processes, or accelerate insight generation
Familiarity with dbt or modern analytics engineering practices
Exposure to machine learning concepts or predictive modeling techniques
Knowledge of regulatory requirements in financial services (e.g., fair lending, FCRA)
Experience presenting insights and recommendations to executive leadership