Principal Software Development Engineer in Test; SDET

Position

Principal Software Development Engineer in Test (SDET)

Full-time

$100,000 - $140,000 a year

10 - 15 years

SDET

• Playwright-Centric UI & Backend Automation Leadership (Core Expectation)
• AI-Driven Quality Engineering (Strategic Expectation)
• Data Quality & Backend Validation (Core Expectation)
• Principal-Level Leadership & Influence
• Domain Experience (Strong Advantage)

10+ years in SDET / Test Automation / Quality Engineering. Demonstrated Principal-level influence across platforms, teams, and architecture decisions. Deep hands‑on experience designing and scaling Playwright‑based automation frameworks. Proven experience integrating AI‑assisted capabilities into modern test frameworks.

We are seeking a Principal Software Development Engineer in Test (SDET) to lead and modernize quality engineering across UI, API, services, and data layers, with a strong emphasis on Playwright‑based and AI‑enabled automation frameworks.

• Defining next-generation automation standards
• Enabling continuous testing in Azure Dev Ops
• Embedding AI‑assisted and agent‑driven quality practices across teams

You will partner closely with engineering, product, Dev Ops, and business stakeholders to drive quality by design, reduce production risk, and enable high‑confidence releases in complex, data‑driven systems—preferably within wealth management or financial services environments.

• Lead the design and automation of data quality validation frameworks across:
  • Databases
  • System-to‑system integrations
• Validate and automate checks for:
  • Data completeness, accuracy, consistency, and reconciliation
  • ETL / ELT transformations
  • Batch jobs, scheduled processes, and file-based integrations (CSV / JSON / XML)
• Build reusable data validation utilities using SQL and Python/Java
• Implement automated reconciliation for financial or transactional data where applicable
• Integrate data quality tests into CI/CD pipelines or scheduled automation runs with actionable reporting
• Proactively identify data anomalies and quality risks before production releases
• Drive quality engineering practices within Azure environments
• Design and govern test execution strategies in Azure Dev Ops
• Define test stages, quality gates, and reporting standards across pipelines
• Ensure traceability across requirements, tests, defects, and releases

• Champion the use of AI‑assisted tools (Git Hub Copilot, Copilot agents, AI assistants) to:
  • Accelerate test case generation
  • Improve automation code quality and maintainability
  • Analyze test failures, logs, and quality trends
• Define guardrails and best practices for responsible AI usage in QA
• Drive adoption of AI‑enabled productivity patterns across QE teams