Senior Associate - Quality Engineer, AI & Automation

Location: New York

Job Description

Requisition ID94490

Department Tech Data AI Ventures Job Function Tech Data AI Ventures Location New  York,New York,United States Role Location Designation Hybrid - 3 days per week Location Designation:
Hybrid - 3 days per week

Role Overview New York Life is seeking a Senior Associate, Quality Engineer to help build modern, automation-first quality practices across our Wealth Management technology platforms. This is a hands-on engineering role for someone who can code, understand the business, challenge designs, and use AI-enabled tooling to improve how quality is built into software from the first requirement through production release.

This is not a manual testing role.

The right candidate will design and build automated test frameworks, review developer unit test strategies, improve CI/CD quality gates, analyze defect patterns, and partner with engineers and product owners to make systems more testable, observable, resilient, and business-ready.

You will work across advisor, client, account, portfolio, transaction, data, integration, and reporting workflows that support wealth management outcomes in a regulated financial services environment. The role requires enough business fluency to know where quality risk hides: in account data, house holding, balances, holdings, transactions, suitability-sensitive workflows, integrations, reports, and downstream advisor/client experiences.

What You’ll DoDesign, build, and maintain automated test suites across API, UI, integration, data, regression, and end-to-end workflows.

Write clean, maintainable automation code using modern engineering practices, including reusable libraries, test utilities, fixtures, mocks, service virtualization, and test data management.

Use AI and GenAI-enabled tools to accelerate test design, coverage analysis, defect triage, test data generation, regression optimization, and failure pattern detection.

Partner with software engineers to review unit test strategy, code coverage, edge-case coverage, mocks/stubs, contract tests, and test results before code moves downstream.

Participate in design and architecture reviews to improve testability, observability, reliability, determinism, data validation, resiliency, and operational supportability.

Build automation into CI/CD pipelines so quality signals are fast, visible, repeatable, and actionable.

Develop automated quality gates for pull requests, builds, deployments, APIs, data contracts, and release readiness.

Analyze recurring defects and production incidents to identify systemic quality gaps and drive root-cause prevention.

Create dashboards and reporting that show meaningful quality health: automation coverage, failure trends, flaky tests, escaped defects, regression duration, release confidence, and risk hotspots.

Collaborate with Product, Engineering, Architecture, Dev Sec Ops , Release Management, and business stakeholders to define test strategy for complex wealth management features.

Translate business scenarios into automation coverage that reflects how advisors, clients, operations teams, and downstream systems actually use the platform.

Help raise the engineering bar by mentoring peers on automation design, test strategy, AI-assisted quality practices, and quality-by-design thinking.

AI & Technical Expectations The ideal candidate should be comfortable using AI as an engineering accelerator—not as magic dust sprinkled on stale test cases.

Expected hands-on capabilities include:

Applying GenAI tools responsibly to generate, refactor, review, and maintain automation code.

Using AI to summarize failures, cluster defects, detect flaky tests, identify regression risk, and improve coverage.

Understanding prompt design, evaluation, reproducibility, privacy constraints, and human review when using AI in a regulated environment.

Building or integrating automation utilities that leverage LLMs, embeddings, or intelligent heuristics where appropriate.

Validating AI-assisted outputs rather than blindly trusting them.

Working with APIs, SQL/data validation, CI/CD pipelines, source control, test frameworks, and cloud or containerized environments.

What Success Looks Like Increased…