Job Title: Automation Test Lead/Manager
Skills: Test Management, Automation Testing (Playwright/Selenium/TOSCA), Snowflake (SQL validation, ETL/pipeline reconciliation), Test Strategy, Data Validation, etc
Experience: 12+years
Hiring Type: Video Interview
Job Type: Long Term
Job Location: Houston, TX (Day One Onsite Job)
NOTE: Locals are highly recommended.
Role Summary
This role owns delivery for a QA managed services organization supporting a Sustainability Energy Trading and Risk Management (ETRM) platform. The person in this seat runs a multi-project team spanning functional testing, automation, and data validation, and is accountable for SLAs, quality metrics, staffing, and client governance across the account. The role blends people leadership, hands-on technical direction across automation and data platforms, and domain depth in energy/sustainability trading and emissions workflows.
- Delivery and people leadership SLAs, governance cadence, staffing, escalations
- Automation testing Playwright/Selenium/TOSCA, CI/CD, API validation
- Snowflake SQL validation, ETL/pipeline reconciliation
- Power BI dashboard and report accuracy validation tied back to Snowflake data
- Sustainability ETRM domain trade/position/settlement concepts plus emissions and ESG reporting workflows
Delivery & People Leadership
- Run a QA managed services organization covering functional testing, automation, and data validation practice areas.
- Own commitments and SLAs, staffing and capacity planning, budgets, and quality standards for the account.
- Run the governance cadence: weekly project reviews with leads, monthly quality/metrics reviews, and quarterly business reviews with client stakeholders.
- Track defect leakage, test coverage, automation pass rates, and cycle time; act on adverse trends before they become client escalations.
- Own escalations end-to-end, from first signal through root cause and corrective action plan.
- Forecast staffing demand a quarter ahead and reallocate people across projects as priorities shift.
- Support presales and scope expansion with effort estimates, staffing models, and test approach.
Automation & Test Strategy
- Set technical direction for automation and continuous testing across the portfolio.
- Own test strategy, test planning, test case design, test data setup, defect management, and configuration management.
- Maintain and grow automated regression coverage using tools such as Playwright, Selenium, and/or TOSCA.
- Run performance, load, and non-functional testing (e.g., JMeter) where required.
- Validate REST/SOAP APIs and integrate automated checks into CI/CD pipelines (Jenkins, GitHub Actions).
- Manage source control, branching, and pull request workflows in GitHub.
- Work with AWS-hosted environments for test execution and data validation (e.g., S3, EC2, Lambda, or equivalent services depending on the platform's architecture).
Data Validation: Snowflake & Power BI
- Write and review SQL-based validation queries against Snowflake to confirm data accuracy, completeness, and consistency across source and downstream systems.
- Validate ETL/ELT pipelines feeding Snowflake, including reconciliation between source systems and the warehouse.
- Review and validate Power BI reports and dashboards for data accuracy, ensuring metrics tie back to underlying Snowflake data.
- Design and enforce value-level and null checks across data pipelines, not just field-existence checks.
Domain: Sustainability & ETRM
- Apply working knowledge of Energy Trading and Risk Management (ETRM) concepts: trade capture, positions, settlements, and risk reporting.
- Understand sustainability and emissions-reporting workflows (e.g., emissions monitoring, carbon accounting, ESG data flows) well enough to define acceptance criteria and test coverage without handholding from subject matter experts.
- Translate business and regulatory reporting requirements into test scenarios that catch real data and calculation errors.
AI-Assisted Testing
- Set standards for how AI coding/testing tools are used in delivery: what can be AI-generated, what always requires human review, and what stays off the tools entirely.
- Own quality checks on AI-generated test artifacts to ensure they validate actual business logic and data correctness.