Lead Data Engineer, Lending Club

• 7+ 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

Desirable

• 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
• 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