Current Employees of Happen Bank: Please apply via your internal Workday Account
Happen Bank (formerly LendingClub) is built around a simple purpose: to clear the way to help people turn intention into action, and action into financial progress. That means offering focused products, a frictionless mobile-first experience, and clear terms with no gotchas. Respect and fairness is part of our DNA, and that ideal shapes how we work, how we treat each other, and how we invest in our employees and our community. Join us in using data, bold thinking, and a commitment to innovation to help clear the way for millions of Americans to achieve more.
About the Role
What You'll Do
- Lead a cross-functional scrum team of onshore and offshore data engineers, setting priorities and removing blockers to deliver reliable data products
- Own the architecture, maintenance, and continuous improvement of critical financial data pipelines that support GL automation, investor reporting, tax documents, and collections workflows
- Partner with finance, accounting, and operations stakeholders to translate business requirements into scalable technical solutions
- Drive adoption of modern data platform capabilities, including Databricks, dbt, Elementary, and Dagster, while maintaining existing production systems
- Identify opportunities to leverage AI tools for QA automation, code review, performance optimization, and documentation standardization
- Build and improve monitoring, alerting, and observability practices to ensure pipeline reliability and rapid incident response
- Establish coding standards and data quality frameworks that scale across the team and reduce operational overhead
- Use AI to create intelligent runbooks, diagnostic agents, and self-service tools that empower L1 support and accelerate troubleshooting
About You
- 8+ years of experience in data engineering, with 2+ years leading teams or projects in a technical lead capacity; bachelor's degree in a related field; or equivalent work experience
- You have hands-on experience using AI tools to accelerate your work and improve output quality — you're equally comfortable using them yourself and showing colleagues how, and you're thoughtful about limitations and where human judgment matters most
- Strong expertise in SQL, data modeling, data warehouse concepts, and building production data pipelines at scale
- Experience with orchestration tools such as Airflow, Oozie, or Dagster and distributed processing frameworks like Spark or PySpark
- Working knowledge of AWS services (EMR, S3, Redshift) and modern data platforms such as Snowflake or Databricks
- You take ownership of outcomes, proactively identifying risks and workflow improvements before they become blockers