Principal Software Development Engineer in Test (SDET)

Ztek Consulting; Trenton (NJ); On-site; USD 100,000 - 140,000; Full time

Job summary

A technology consulting firm in Trenton, NJ, seeks a Principal Software Development Engineer in Test (SDET) to lead modern quality engineering practices focused on UI, API, and data automation. Ideal candidates will have 10+ years in SDET/Test Automation, expertise in Playwright frameworks, and skills in AI-driven tools. Responsibilities include defining automation standards, integrating quality checks into CI/CD pipelines, and driving quality practices in Azure environments. Competitive salary offered.

Qualifications

• 10+ years in SDET, Test Automation, or Quality Engineering.
• Experience with Playwright-based automation frameworks.
• Ability to integrate AI-assisted capabilities into test frameworks.

Responsibilities

• Lead and modernize quality engineering across UI, API, services, and data layers.
• Define and enable next-generation automation standards.
• Integrate data quality tests into CI/CD pipelines.

Skills

SDET

AI-Driven Quality Engineering

Data Quality Validation

Automation Standards

Continuous Testing

Playwright Framework

Tools

SQL

Python

Java

Azure DevOps

Job description

Position

Principal Software Development Engineer in Test (SDET)

Position Type

Full-time

Salary Range

$100,000 - $140,000 a year

Years of Experience

10 - 15 years

Desired Skills

SDET

Job Description

• 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)

Experience Profile

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.

About the Role

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.

Hands‑on Technical Leadership Responsibilities

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

You will partner closely with engineering, product, DevOps, 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.

Data Quality & Backend Validation (Core Expectation)

• 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 DevOps
• Define test stages, quality gates, and reporting standards across pipelines
• Ensure traceability across requirements, tests, defects, and releases

AI-Driven Quality Engineering

• Champion the use of AI‑assisted tools (GitHub 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