Sdet 2

Bidgely

About The Position

Bidgely is an AI-powered SaaS Company accelerating a clean energy future by enabling energy companies and consumers to make data-driven energy-related decisions. Ranked #7 in Applied AI on Fast Company’s list of Most Innovative Companies in the World, Bidgely is putting customers at the center of the clean energy future. Powered by our unique patented technology, Bidgely's UtilityAI™ Platform transforms multiple dimensions of customer data - such as energy consumption, demographics, and interactions into deeply accurate and actionable consumer energy insights. We leverage these insights to empower each customer with personalized recommendations tailored to their individual personality and lifestyle, usage attributes, behavioral patterns, purchase propensity and beyond. From a distributed energy resources (DER) and grid edge perspective, Bidgely is advancing smart meter innovation with data-driven solutions for solar PVs, electric vehicle (EV) detection, EV behavioral load shifting and managed charging, energy theft, short-term load forecasting, grid analytics and time of use (TOU) rate designs. Bidgely’s UtilityAI™ energy analytics provides deep visibility into generation and consumption for better peak load shaping and grid planning and delivers targeted recommendations for new value-added products and services.

Requirements

5–8 years of experience in software quality engineering, with a strong track record of owning quality for complex features or services.
Hands-on expertise in test automation and framework design using Java and/or Python — not just scripting, but designing maintainable, scalable test architectures.
Strong experience in API and backend testing for distributed systems and microservice architectures.
Experience integrating automated tests into CI/CD pipelines and enforcing quality gates.
Demonstrated ability to analyze complex problems, perform RCA, and communicate solutions clearly.
Practical, demonstrable experience using GenAI tools in SDET/testing workflows — this is a must-have, not a nice-to-have.
Strong understanding of test design techniques (equivalence partitioning, boundary analysis, risk-based testing) and when to apply them.
Experience with bug tracking, test planning, estimation, and release management processes.
Degree in Computer Science/Engineering, or equivalent professional experience.

Nice To Haves

Experience with SQL/NoSQL databases, big data technologies, or data-intensive systems testing.
Exposure to AWS services (S3, SQS, EC2, EMR, Redshift) and cloud-native testing patterns.
Experience with performance testing of APIs and services (JMeter, K6, Gatling, or similar).
Familiarity with contract testing (Pact, Spring Cloud Contract) or service virtualization.
Experience defining and tracking quality KPIs and health metrics at a team or product level.
Understanding of business intelligence and data platforms.
Exposure to monitoring/observability tools (Datadog, Grafana, CloudWatch) for production quality insights.

Responsibilities

Own test strategy and execution for assigned product areas, covering API, backend, and integration layers with a clear risk-based approach.
Design and maintain test plans that cover functional, non-functional, and edge-case scenarios — aligned with the test pyramid (unit → integration → e2e).
Define and track quality metrics (escaped defects, test effectiveness, automation coverage, flakiness) and use data to drive release-readiness decisions.
Participate in design and architecture reviews to provide testability and risk input early in the development cycle (shift-left).
Build, extend, and maintain automation frameworks for API and service-level testing with a focus on reliability, speed, and maintainability.
Write clean, production-grade test code in Java or Python — following the same engineering standards as application code.
Integrate automated tests into CI/CD pipelines (Jenkins, GitHub Actions) and enforce quality gates that provide fast, actionable feedback.
Own test infrastructure decisions: parallelization, test data management, environment provisioning, and flaky test management.
Actively use GenAI tools (e.g., Claude, Copilot, or similar) in daily testing workflows — test case generation, code authoring, failure analysis, data validation scripting, and exploratory test ideation.
Evaluate and adopt AI-assisted testing techniques for areas such as test generation from requirements/specs, intelligent test selection, and automated root-cause analysis of failures.
Bring practical experience integrating GenAI into quality processes — not just awareness, but demonstrated usage that improved efficiency, coverage, or signal quality.
Stay current on emerging AI capabilities relevant to SDET practices and proactively propose adoption where ROI is clear.
Work closely with product, business, and engineering teams to understand requirements and translate them into comprehensive test coverage.
Conduct thorough bug triage and RCA (Root Cause Analysis), communicating findings clearly with actionable recommendations.
Contribute to sprint planning with realistic test effort estimates, risk assessments, and dependency identification.
Mentor junior SDETs and QA engineers on testing best practices, framework usage, and quality mindset.