Engineering Manager- Data and Applied ML

Engineering Manager
- Data and Applied ML

Banking is being reimagined—and customers expect every interaction to be easy, personal, and instant
.

We are building a universal banking assistant that millions of U.S. consumers can use to transact across all financial institutions and, over time,
autonomously drive their financial goals
. Powered by our proprietary BankGPT platform
, this assistant is positioned to displace age-old legacy systems within financial institutions and own the end-to-end CX stack
, unlocking a $200B opportunity and potentially replacing multiple publicly traded companies
.

Ultimately,
our mission is to drive financial well-being for millions of consumers.

With over two-thirds of Americans living paycheck to paycheck, 50% holding less than $500 in savings, and only 17% financially literate,
we aim to
put financial well-being on autopilot to help solve this problem.

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

• 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.

• 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.
• Lead teams building applied ML models, analytics, and experimentation frameworks
  .
• Collaborate with data scientists to product ionize 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
  .

• 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.

• 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.

• 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.

• 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…