Engineering Manager- Data and Applied ML

About the Role

We are hiring a deeply technical, hands-on Engineering Manager to lead our Data Engineering, Data Platform, and Applied Machine Learning / Data Science efforts.

This is a builder-first leadership role. You will design, build, and operate critical data and ML systems while leading a team of senior engineers and data scientists and work with the stakeholders for setting technical direction. You will own the data and ML foundations that power analytics, experimentation, AI-driven products, and autonomous systems across the company.

What You’ll Do

• Hands-On Architecture & Engineering

  • Design and build data pipelines supporting high-volume, low-latency workloads.
  • Architect end-to-end data and ML systems across ingestion, transformation, storage, feature generation, and serving layers.
  • Write and review production-quality code, guiding schema design, partitioning, and performance tuning.
  • Debug complex issues across data correctness, model performance, latency, and system scalability.
  • Make architectural trade-offs between lake house, warehouse, streaming, and real-time inference systems.
    
    

  Data Platform Ownership

  • Own and evolve the core data platform supporting analytics, experimentation, and ML workloads.
  • Build and operate modern data systems using distributed compute, streaming platforms, and cloud-native storage.
  • Design feature pipelines and data services consumed by ML models and product teams.
  • Implement semantic layers and data APIs to ensure metric consistency and reuse.
  • Partner with infrastructure teams on reliability, capacity planning, and cost optimization.
    
    

  Machine Learning & Data Science Leadership

  • Lead teams building applied ML models, analytics, and experimentation frameworks.
  • Collaborate with data scientists to productionize models, from offline training to online inference.
  • Own ML data workflows including feature engineering, model evaluation, monitoring, and retraining pipelines.
  • Enable experimentation platforms, A/B testing, and feedback loops for continuous learning.
  • Drive best practices around model performance, bias detection, and explainability.
    
    

  Quality, Governance & Observability

  • Establish data and ML quality standards, validation, and anomaly detection.
  • Implement observability across pipelines and models (metrics, alerts, drift detection).
  • Enforce data governance, PII handling, access controls, and auditability.
  • Define SLAs/SLOs for data freshness, model reliability, and system availability.
  • Partner closely with Security and Compliance teams to meet regulatory requirements.
    
    

  Technical Leadership & People Management

  • Lead and mentor data engineers, ML engineers, and data scientists.
  • Set technical standards for architecture, code quality, testing, and documentation.
  • Drive sprint planning, execution, and delivery accountability.
  • Hire, onboard, and grow senior engineers and scientists capable of owning complex systems.
  • Foster a culture of ownership, rigor, and continuous technical improvement.
    
    

  Cross-Functional Collaboration

  • Work closely with Product, AI, Platform, Security, and Compliance teams
  • Translate business and product requirements into scalable data and ML systems.
  • Communicate architectural decisions, risks, and trade-offs clearly to leadership.

Required Qualifications

• 8+ years of experience building data-intensive and ML-driven systems.
• 2+ years of experience managing engineers and/or data scientists while remaining hands-on.
• Strong expertise in programming languages like Node.js, Python or Golang; experience with distributed data processing frameworks.
• Hands-on experience with streaming systems and real-time data processing.
• Experience designing and operating data lakes, warehouses, or lake house architectures.
• Experience supporting ML training, feature pipelines, and online inference in production.
• Deep understanding of data modeling, performance optimization, and system reliability.
• Strong debugging and operational experience in cloud environments.
  

Preferred Qualifications

• Experience enabling AI-first or ML-heavy products.
• Familiarity with experimentation platforms, model evaluation, and monitoring.
• Experience in regulated or enterprise-scale environments.
• Prior background as a Staff or Principal Engineer before moving into management.

What Makes This Role Special?

• You’ll shape the core AI that powers agentic intelligence for financial systems serving millions of users.
• You’ll own a research-meets-engineering mandate — from exploring new models to bringing them to life in production.
• You’ll define how autonomous AI systems learn, adapt, and remain safe in a regulated environment.
• You’ll work with a team combining AI research, applied data science, and product engineering, moving fast with purpose and rigor.

We value in-person time for planning, problem-solving, and connection. Team members in this role must live within commuting distance of our Palo Alto or San Francisco hub.

Compensation

•  Base salary is expected to be between $170,000 - $190,000+ bonus+ Pre-ipo options. Exact compensation may vary based on skills and location.

What We Offer

• 💡 100% paid health, dental & vision care
• 💰 401(k) match & financial wellness perks
• 🌴 Discretionary PTO + paid parental leave
• 🧠 Mental health, wellness & family benefits
• 🚀 A mission-driven team shaping the future of banking