Role Description We are hiring a Senior QA Engineer to own and scale quality processes across Volt's platform, infrastructure, and customer-facing systems. This role carries ownership: defining test strategy, building automation infrastructure, driving release confidence, and setting the quality bar for the engineering team. You will work closely with Engineering, Product, Customer Operations, and Leadership to improve quality standards and reduce production risk as the platform scales rapidly. This is not a traditional manual-only QA role. We're looking for someone who thinks like a systems builder and who can define strategy, build automation infrastructure, and operate with enough technical depth to own QA environment management within our AWS infrastructure, without needing to wait on a dedicated DevOps resource. Just as important: this is a team role. Our engineering group is small, tight-knit, and collaborative. We cover for each other, give candid feedback without ego, and take pride in raising the collective quality of the work. We're looking for someone who adds to that culture, someone who wants to build great things alongside great people. The ideal candidate is highly organized, technically deep, process-oriented, and comfortable operating with senior-level ownership in a startup environment where systems are evolving quickly. What You'll Do - Own and continuously improve QA strategy, processes, and standards across engineering and product workflows - Design and build automated testing infrastructure for platform reliability and release confidence - Define and maintain test plans, regression testing, and release validation processes - Identify gaps in testing coverage and operational risk areas, and drive resolution - Partner with Engineering and Product to set and enforce quality gates in the development and release workflow - Own staging and QA environment management within AWS, including deployment of code into pre-production environments - Document bugs clearly with reproducible steps and business impact context - Drive observability, monitoring validation, and production issue analysis - Lead API testing, workflow testing, and operational scenario validation with an emphasis on back-end platform and infrastructure coverage over UI testing - Actively support peers through knowledge sharing, filling coverage gaps, and contributing to team-wide quality standards - Leverage AI tooling to improve test generation, bug analysis, automation, documentation, and troubleshooting - Build and maintain scalable QA documentation, standards, and operational playbooks AI Expectations for This Role AI is not an add-on at Volt, it's woven into how we build and test. We're excited about what's possible when strong QA fundamentals meet modern AI-enabled workflows, and we're looking for someone who shares that excitement. Ideally, you've moved beyond dabbling, you've been part of a team that has transitioned to AI-augmented QA practices, and you can bring that experience with you: what worked, what didn't, and how to make it stick. What that looks like in practice: - Systematic use of AI-assisted test generation and coverage analysis - AI-assisted bug analysis and triage - Automated regression coverage at scale - Release validation automation - Test documentation generation - Operational workflow simulations - Root-cause analysis support - Helping the broader team grow its own AI-enabled QA practices over time What We're Looking For - Proven experience in senior QA engineering, software testing, or release management - Strong understanding of QA methodologies, testing lifecycles, and test strategy at the system level - Experience building and owning automated testing frameworks and infrastructure - Strong API testing and back-end platform testing experience - Ability to think strategically about operational risk, edge cases, and quality at scale - Comfort with AWS environments and the ability to manage deployments into staging and QA environments independently - Strong documentation and communication skills - Experience working cross-functionally with Engineering and Product teams - Ability to operate independently and drive quality initiatives while actively supporting and collaborating with a small, high-trust team - A collaborative, low-ego approach to peer feedback and shared ownership of quality - Comfortable operating in fast-moving startup environments with high ownership expectations - Strong troubleshooting and analytical mindset - Actual, hands-on experience with AI-enabled testing workflows and agentic tooling Nice to Have - Experience testing high-volume platforms or messaging systems - Hands-on experience with AWS services relevant to deployment and environment management - Familiarity with CI/CD pipelines and deployment workflows - Experience with observability and monitoring platforms - Experience with performance or load testing at scale - Familiarity with SQL and database validation workflows - Experience building QA automation infrastructure from the ground up - Deep familiarity with agentic tooling and AI-assisted QA at the team or org level How We Work - Remote-first team with high ownership and accountability - Small, highly collaborative environment - Fast iteration cycles and direct access to leadership - Strong builder culture with low ego and a commitment to peer feedback - Bias toward action, clarity, and operational excellence - AI-forward engineering mindset, actively evolving our practices Why Volt - Opportunity to build and own foundational QA systems during a major growth stage - High-impact role with visibility across the business and direct influence on engineering quality - Ability to define quality standards and shape engineering maturity - Work closely with experienced founders and technical leadership - Help improve reliability for mission-critical messaging infrastructure - Join a team that invests in each other and works hard to get better together Location Remote (U.S. preferred) Salary Range $115,000 to $140,000

Role Description Performs deep functional, exploratory, and customer-focused testing for new features and production issues. Key Responsibilities - Execute manual testing - Create high-quality test cases (Test Plans, traceability) - Validate backend data using: - SQL Server Management Studio - Validate file integrations using: - Using internal tools - Capture evidence and defects: - Using internal tools - Perform: - Exploratory testing - Feature Testing - Regression Testing - Assist with UAT as needed - Workflow validation across distributed systems - Validate document outputs and compliance workflows using: - Azure Document Intelligence - Reproduce and validate customer issues - Coordinate with automation engineers to develop and review tests – In addition, has the desire to learn automation with AI tooling - Front line support for hot fixes and patches - Demo new features and fixes to Compliance, Customer or Product teams Qualifications - Strong manual testing experience - Excellent attention to detail - Ability to test complex workflows Requirements - Mortgage/FinTech experience (Nice to Have)

• Execute and monitor pipelines in Azure DevOps • Categorize failures: Test defect vs environment vs data vs code • Script-Level Debugging & Root Cause Analysis Debug automation scripts written in: • Playwright / Selenium Identify root causes such as: • Flaky selectors (dynamic DOM changes) • Synchronization/timing issues (wait conditions) • Assertion mismatches • Data dependency failures • Validate API failures using: • Postman or GraphQL tools • Inspect logs and telemetry (App Insights, pipeline logs) • Track trends and provide triage reporting

• Shift quality left, properly. Help teams catch defects at the cheapest possible moment: during refinement and task definition, before a line of code exists. Join story-writing and design discussions, ask the questions that surface ambiguity and edge cases early, and coach teams to build testability and clear acceptance criteria into the work from the start. • Lift the whole testing pyramid. Work with developers across the full range, from unit and integration tests through to end-to-end and system testing. Help them write tests that are fast, deterministic, and worth trusting; rebalance suites that are top-heavy or flaky; and establish what to test at each level, and why. • Make pipelines fast and cheap. Treat CI as a product. Profile and optimise pipeline definitions, cut wasted compute, parallelise and shard test execution sensibly, and shorten feedback loops so teams get a trustworthy signal in minutes, not hours. Help them see the time and cost their pipelines actually incur, and bring both down. • Bring AI into the quality toolkit. Help teams get real value from AI-assisted development: generating and maintaining tests, accelerating code review, and shortening feedback loops, while keeping a critical eye on what these tools produce so quality and trust do not slip. You champion these tools and show teams how to use them well. • Mentor and enable. This is the heart of the job. Pair with engineers, run workshops, review test code and pipeline changes, and leave behind teams that hold a higher quality bar on their own after you have gone. Success is measured by how much better the team is, not by how much you personally produced. • Own quality strategy across the SDLC. Assess where a team or programme stands, agree a pragmatic plan to improve it, and drive that plan through delivery: test strategy, environments and test data, release confidence, and the metrics that tell everyone whether quality is moving in the right direction.