Manager, Software Development Engineer-SDET Automation Testing

Key Responsibilities:

​ ​ • Design, build, and maintain scalable test automation frameworks that cover frontend, backend, API, and data validation layers across investment research platforms. • Own the end-to-end quality strategy for research technology initiatives — defining test plans, coverage targets, and risk-based testing approaches from discovery through production. • Develop and execute automated regression, smoke, integration, contract, and performance test suites as a first-class part of CI/CD pipelines — integrating quality gates, test reporting, and failure analysis tooling throughout. • Partner with product owners, architects, and engineers to translate business and technical requirements into testable acceptance criteria and executable test cases. • Validate cloud-ready, event-driven services and integration patterns with a focus on reliability, data correctness, and edge case coverage. • Drive shift-left testing practices by embedding quality gates early in the development cycle and advocating for testability in design and code reviews. • Design and execute chaos engineering experiments to validate system resilience — simulating failure conditions such as service outages, network degradation, and dependency unavailability to harden platform reliability. • Design and execute automated test suites for containerized workloads, validating Docker image integrity, deployment configurations, health checks, and service behavior across isolated and integrated environments. • Build observability validation into delivery and diagnose complex production defects — ensuring alerting, dashboards, and logging behave correctly, and improving long-term resiliency through targeted test coverage. • Validate data pipelines and ETL workflows on cloud platforms (GCP, Snowflake), including data transformation accuracy and pipeline reliability. • Mentor engineers on quality practices, test design principles, and automation patterns — helping the team build confidence in what they ship. Incorporate approved Generative AI capabilities to enhance test coverage, test data generation, and quality productivity while following enterprise governance and compliance expectations. ​

Bachelor's degree in Computer Science, Information Systems, Engineering, or a related technical field, or equivalent practical experience. • 6+ years of SDET or quality engineering experience, including hands-on test automation development in Python. • Proven track record building and maintaining automation frameworks for UI and API layers, with hands-on experience integrating automated test gates into CI/CD pipelines. • Strong experience testing event-driven microservices and APIs — including async message flow validation, contract testing, and integration behavior under failure conditions. • Experience with chaos and resilience testing, including designing fault injection scenarios and interpreting system behavior under degraded conditions. • Hands-on experience testing containerized applications — validating workloads running as Containers, including configuration, networking, and service-level correctness. • Solid working knowledge of relational and NoSQL databases, with the ability to write data validation queries and partner with data engineers to verify analytical pipelines, dashboards, and reporting outputs. • Experience with performance and load testing — defining baselines, running tests, and interpreting results to inform architecture decisions. Working knowledge of secure coding and static analysis tools and how to interpret and act on findings.

Preferred Qualifications:

• Experience in quality engineering within a regulated industry — financial services background is a strong plus. • Familiarity with testing rules engines and validating large-scale time-series or data lake outputs. • Experience collaborating with quantitative or data science partners to validate model outputs and operationalized pipelines. • Demonstrated ability to balance sprint-level delivery with longer-term test architecture evolution and platform scalability. Strong communication skills — comfortable translating test coverage gaps and quality risks into clear language for both technical and business stakeholders.