Designs and builds data platforms, tools, and solutions that enable the Bank to manage, secure, and extract value from its data, providing technical leadership across multiple teams.
Compensation
The base salary for this position is reflective of the range of salary levels for all roles within this pay grade across the U.S. Individual salaries within this range will vary based on factors such as role, relevant skillset, relevant experience, education and geographic location. In addition to the base salary, this role is eligible to participate in an incentive compensation plan, with any such payment based upon company, line of business and/or individual performance.
Responsibilities
Design, develop, and support scalable data solutions, APIs, tools, and processes
Work closely with IT application teams, Enterprise Architecture, infrastructure, information security, and LOB stakeholders
Act as a principal technical expert and trusted advisor
Conduct research and development on current trends and technologies
Build and maintain CI/CD pipelines and self-service deployment tools
Requirements
Bachelor's degree in Computer Science, Information Systems, or related field
Nice to have: modern data platforms
Nice to have: cloud-native architectures
Nice to have: data transformation technologies (Snowflake, dbt)
Nice to have: traditional data engineering tools (IBM DB2, IBM Information Server/DataStage)
Design, build, and operate backend services in the Applied AI stack including APIs, data models, background jobs, and pipelines.
Locations (3)
San Francisco, California, United States
New York City, New York, United States
London, England, United Kingdom
Compensation
Bi-annual performance bonus structure; Generous equity grant vested over 4 years; Up to $15k Relocation bonus; $10K housing bonus (if you live within 0.5 miles of our office); $1.5K monthly stipend for meals; Free Equinox membership; $200 monthly laundry reimbursement; $200 monthly personal wellness reimbursement; Health, Dental, Vision insurance
Responsibilities
Design, build, and operate backend services in the Applied AI stack including APIs, data models, background jobs, and the pipelines that connect them.
Own features end to end: scope the problem, write the design, ship the code, instrument it, and keep it healthy in production.
Build and tune high-throughput data and job pipelines: queuing, batching, idempotency, retries, and backpressure.
Make the system fast and reliable by adding failure recovery in pipelines, profile hot spots, Agent token and cost attribution, caching issues, and set latency, error, cost budgets you actually hold to.
Instrument what you ship: observability, metrics, logging, tracing, and alerts that proactively catch problems.
