The posting
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 is building a modern enterprise data and AI platform on Databricks. Our Data Engineers, Data Scientists, and Analytics teams build the pipelines. This team builds what they build on.
As a Principal Data Engineer, you'll own the technical architecture and development of the platform frameworks and infrastructure that make downstream pipelines faster, safer, and more consistent. This includes configuration-driven ingestion frameworks, secure connectivity to source systems, reusable data protection libraries for anonymization and tokenization, and the infrastructure-as-code foundation that brings these capabilities together.
This is a framework and platform engineering role, not a pipeline-delivery role. Success means enabling teams to onboard new data sources, protect sensitive information, and promote work to production through configuration rather than custom code.
What You'll Do
- Own ingestion architecture: Define the technical vision and architecture for a declarative, configuration-driven ingestion framework that brings data from relational databases and object-store files into the raw data layer. Establish reusable patterns and contracts for downstream teams while evaluating tradeoffs between ingestion approaches
- Build scalable platform frameworks: Lead the design and implementation of reusable, versioned libraries that support policy-driven data protection, including configurable rules, governed access, consistent outputs, secure-by-default failure behavior, and audit trails. Maintain backward compatibility across shared capabilities used by multiple teams
- Set infrastructure and deployment standards: Define infrastructure-as-code practices for safe deployment, replacement, rollback, and lifecycle management, including data ownership considerations for managed pipeline objects. Establish compute, cost, and sizing guidance for large initial loads and steady-state incremental processing at billion-row scale
- Drive platform reliability and observability: Establish monitoring, alerting, reconciliation, recovery, and data validation capabilities that ensure reliable operations from source to destination. Identify and address observability gaps in underlying platform tools
- Lead evidence-based technology decisions: Conduct structured proofs of concept with measurable success criteria, document architectural decisions and tradeoffs, and engage technology vendors to resolve product gaps and influence future capabilities
- Drive cross-functional platform delivery: Translate ambiguous technical challenges into clear requirements, interfaces, dependencies, and phased delivery plans. Partner with Data Engineering, Data Governance, Information Security, DBAs, and source-system owners to deliver early solutions while building scalable long-term capabilities and driving framework adoption
- Raise engineering standards: Mentor senior and staff engineers, lead technical design and code reviews, and establish best practices for testing, documentation, release management, and maintainability across shared platform capabilities
About You
- 10+ years of software or data platform engineering experience, including designing and delivering platform-level frameworks, libraries, or infrastructure used across multiple engineering teams
- Bachelor's degree or higher, or equivalent combination of education and experience
- Strong experience with infrastructure as code, including Terraform and CI/CD pipelines for data platforms
- Deep experience building ingestion capabilities across diverse data sources, including relational databases, files, APIs, and custom systems, with expertise in incremental processing, change data capture (CDC), schema evolution, and protecting production source systems from excessive load
- Advanced programming skills in Python, SQL, and a JVM language such as Scala or Java, with experience developing Spark UDFs and reusable libraries using strong testing, packaging, and versioning practices
- Deep hands-on expertise with Databricks, including Delta Lake, Unity Catalog, Auto Loader, workflows, compute configuration, and governance capabilities, or equivalent data platform experience
- Strong knowledge of security and privacy engineering for sensitive data, including encryption, key management, tokenization or format-preserving techniques, role-based access controls, and audit logging
- Experience designing and operating large-scale data systems processing billions of rows, including performance testing, cost optimization, monitoring, and recovery strategies
- Excellent written and verbal communication skills, including the ability to develop clear technical designs, architecture decision records, interface contracts, and documentation while influencing senior stakeholders without direct authority
- You navigate ambiguous technical challenges, evaluate tradeoffs thoughtfully, and take ownership of delivering durable solutions that improve engineering effectiveness across teams
- You have deep experience applying AI tools to complex engineering work, including architecture exploration, development, testing, and problem-solving. You help shape how AI is incorporated into engineering workflows while maintaining strong judgment around security, accuracy, maintainability, and where human oversight remains essential
Nice to Have
- Experience in banking, lending, or another regulated environment with model risk management, SOX, or similar audit requirements
- Experience with data quality, lineage, catalog tooling, and data contract approaches
- Prior experience as a Principal Engineer, Staff Engineer, or technical lead for a platform engineering team
- Experience with Databricks Asset Bundles or equivalent deployment tooling
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 197,000-230,000. 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-BC1
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 [email protected].
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.
