Ranked as #12 on Forbes’ List of 25 Fastest Growing Public Tech Companies for 2017, EPAM is committed to providing our global team of over 24,000 people with inspiring careers from day one. EPAMers lead with passion and honesty, and think creatively. Our people are the source of our success and we value collaboration, try to always understand our customers’ business, and strive for the highest standards of excellence. No matter where you are located, you’ll join a dedicated, diverse community that will help you discover your fullest potential.
You are curious, persistent, logical and clever – a true techie at heart. You enjoy living by the code of your craft and developing elegant solutions for complex problems. If this sounds like you, this could be the perfect opportunity to join EPAM as a Lead QA Automation Engineer. Scroll down to learn more about the position’s responsibilities and requirements.
Work closely with Development and QA teams on design and implementation of automated testing framework for Data Quality validation, Regression and Performance testing;
Be hugely involved in setting up of CI/CD practices on project;
Be able to improve testing process and techniques on a project and establish them from scratch. Including communication flows, tracking tools;
Analyze and measure current processes and practices and suggest/drive improvements to time to market/quality/cost;
Utilize metrics and KPI to make decisions and track project risks;
Provide timely input of risks and issues, impacting quality, schedule or resources, and recommend effective mitigation plans when applicable.
3+ years of Software development experience;
5+ years of automation testing experience for multi-tier web based applications build;
Experience in developing test plans and test scenarios;
Experience in setting up and implementing test automation framework;
Experience in automated testing in a continuous integration environment;
Experience in quality assurance of data driven implementations;
Experience working on data warehouse specific projects;
Proficient with SQL and well-formed understanding of data, data modeling, data mining and ETL processes;
Confident Linux user;
Working knowledge of common Big Data technologies: HDFS, YARN, Hive, Spark;
Proficient in some scripting language: Python (preferable), Groovy, Shell;
Expert level of Software Quality Control processes and SDLC. Able to follow, actively influence, enhance and define Software Quality Control processes;
Superior communication skills.
Nice to have
Knowledge of Java and understanding of JVM internals is plus.