Lead Data Engineer

LendingClub; San Francisco (CA); Hybrid; USD 190,000 - 220,000; Full time

Benefits offered by this job

Equity and annual bonus opportunities

Medical, dental and vision plans

401(k) match and wellness programs

Flexible time off and parental leave

Job summary

Happen Bank is hiring a Lead Data Engineer in San Francisco to own and optimize critical financial data pipelines powering month-end close, investor reporting, and revenue recognition. You will lead a mixed onshore/offshore team, collaborate with finance and operations, and advance automation and AI-driven tooling.

You will mentor engineers, implement data quality standards, and drive observability across data workflows in a hybrid work model with in-office days.

Qualifications

• 7+ years of experience in data engineering and 2+ years leading teams.
• Hands-on with AI tools to accelerate work and quality.
• Strong expertise in SQL, data modeling, data warehouse concepts, and scalable pipelines.
• Experience with orchestration tools (Airflow/Oozie/Dagster) and distributed processing (Spark/PySpark).
• Working knowledge of AWS services (EMR, S3, Redshift) and platforms like Snowflake or Databricks.
• Own outcomes, identify risks early, and communicate across teams.
• Experience mentoring offshore teams and working across time zones.

Responsibilities

• Lead a cross-functional scrum team of onshore and offshore data engineers.
• Own architecture, maintenance, and improvement of financial data pipelines for GL automation, reporting, and tax documents.
• Partner with finance, accounting, and operations to translate requirements into scalable solutions.
• Drive adoption of modern data platform capabilities and AI-driven improvements.
• Build monitoring, alerting, and observability for reliability and rapid incident response.
• Establish coding standards and data quality frameworks at scale.
• Create AI-driven runbooks and self-service tools for faster troubleshooting.

Skills

SQL & data modeling

Data pipelines

Leadership

Cloud & AWS

Databricks / Spark

Orchestration (Airflow / Dagster)

dbt

AI tools in development

Data warehousing

Communication

Education

Bachelor's degree in a related field

Tools

Databricks

Dagster

Airflow

Spark

PySpark

Redshift

Snowflake

AWS

dbt

Elementary

Job description

Current Employees of Happen Bank: Please apply via your internal Workday AccountHappen 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 RoleHappen 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 DoLead a cross-functional scrum team of onshore and offshore data engineers, setting priorities and removing blockers to deliver reliable data productsOwn the architecture, maintenance, and continuous improvement of critical financial data pipelines that support GL automation, investor reporting, tax documents, and collections workflowsPartner with finance, accounting, and operations stakeholders to translate business requirements into scalable technical solutionsDrive adoption of modern data platform capabilities, including Databricks, dbt, Elementary, and Dagster, while maintaining existing production systemsIdentify opportunities to leverage AI tools for QA automation, code review, performance optimization, and documentation standardizationBuild and improve monitoring, alerting, and observability practices to ensure pipeline reliability and rapid incident responseEstablish coding standards and data quality frameworks that scale across the team and reduce operational overheadUse AI to create intelligent runbooks, diagnostic agents, and self-service tools that empower L1 support and accelerate troubleshootingAbout You7+ 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 experienceYou 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 mostStrong expertise in SQL, data modeling, data warehouse concepts, and building production data pipelines at scaleExperience with orchestration tools such as Airflow, Oozie, or Dagster and distributed processing frameworks like Spark or PySparkWorking knowledge of AWS services (EMR, S3, Redshift) and modern data platforms such as Snowflake or DatabricksYou take ownership of outcomes, proactively identifying risks and workflow improvements before they become blockersStrong communication and organizational skills with experience collaborating across business, product, and engineering teamsExperience leading or mentoring offshore engineering teams and managing work across time zonesYou balance technical depth with business context, understanding how data pipelines support financial processes and regulatory requirementsNice to HaveExperience with dbt for data transformation and Elementary for data validationHands-on use of AI tools such as Claude, ChatGPT, or GitHub Copilot to improve development workflowsBackground in financial services, accounting systems, or investor reporting processesExperience building self-service analytics tools using Streamlit or similar frameworksFamiliarity with data quality frameworks, alerting systems, and SRE practices for data infrastructureWork LocationSan FranciscoThe 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 RequirementsLocal 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 RequirementsAs needed travel to Happen Bank offices and/or other locations, as needed.CompensationThe 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-JH1Happen 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 [e-mail] on AI Tool UseFor 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.