Lead Data Engineer at LendingClub in San Francisco, CA

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

Happen Bank's Data Operations Center ensures consistent, reliable delivery of data that powers critical business functions across finance, accounting, investor reporting, and collections. As a Lead Data Engineer, you'll lead the Financial Data Operations team, owning the pipelines that support month-end close, investor servicing, revenue recognition, and other high-impact processes that keep the business running smoothly. You'll act as the bridge between engineering, product, and business stakeholders while building team capability and driving operational excellence through smart automation and AI-driven improvements.

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
• Strong communication and organizational skills with experience collaborating across business, product, and engineering teams
• Experience leading or mentoring offshore engineering teams and managing work across time zones
• You balance technical depth with business context, understanding how data pipelines support financial processes and regulatory requirements

Nice to Have

• Experience with dbt for data transformation and Elementary for data validation
• Hands-on use of AI tools such as Claude, ChatGPT, or GitHub Copilot to improve development workflows
• Background in financial services, accounting systems, or investor reporting processes
• Experience building self-service analytics tools using Streamlit or similar frameworks
• Familiarity with data quality frameworks, alerting systems, and SRE practices for data infrastructure

Work Location San Francisco

The above locations are eligible offices for this role. The locations have been determined to foster in-person collaboration with this role’s team or the related business lines. We utilize a hybrid work model, and our teams are in-office Tuesdays, Wednesdays, and Thursdays. In-person attendance is essential for this role’s success, and remote placement will not be considered. Happen Bank offers relocation, based on actual job level.

Time Zone Requirements Local hours (PT)

While the position will primarily work local hours, Happen Bank is headquartered in Pacific Time and our ideal candidate will be flexible working across time zones when necessary.

Travel Requirements As needed travel to Happen Bank offices and/or other locations, as needed.

Compensation The target base salary range for this position is 190,000-220,000. The base salary of the role will be determined by job-related knowledge, experience, education, skills, and location. Base salary is just one part of Happen Bank's Total Rewards package. You may also be eligible for long-term awards (equity) and an annual bonus (which is based on company performance, employee performance and eligible earnings).

We’re creating new financial services solutions for our members based on fairness, simplicity, and heart, and we treat our employees the same way. We offer a competitive benefits package that includes medical, dental and vision plans for employees and their families, 401(k) match, health and wellness programs, flexible time off policies for salaried employees, up to 16 weeks paid parental leave and more.

#LI-Hybrid #LI-JH1

Happen Bank is an equal opportunity employer and dedicated to diversity, equity, and inclusion in the workplace. We do not discriminate on the basis of race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), gender, gender identity, gender expression, sexual orientation, age, marital status, veteran status, disability status, political views or activity, or other applicable legally protected characteristics. We believe that a variety of perspectives will make our teams and business stronger as we work together to transform the traditional banking system.

We are committed to providing reasonable accommodations for qualified individuals with disabilities in our job application process. If you need assistance or an accommodation due to a disability, please contact us at interviewaccommodations@happen.com.

Notice on AI Tool Use

For select roles and locations, candidate interviews may be recorded, transcribed and summarized by tools such as artificial intelligence (AI) to assist our hiring managers with the application process.

You will have the opportunity to opt out of recording, transcription, and summarization prior to any scheduled interviews. We will not discriminate against you if you choose to opt out.

During the interview, we will collect the following categories of personal information from or about you: contact information, identifiers, professional and employment-related information, sensory information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment.

We will only share your interview, transcription, or summary with persons whose expertise or technology is necessary to process your application, evaluate your fitness for a position, and administer or support the tool. We will not sell your personal information or disclose it to any third party for their marketing purposes. For more information about how we will handle your personal information, please refer to our Privacy Disclosure.

We will delete any recording of your interview promptly but in no event later than 30 days after making a hiring decision.