Infoworks Technologies Private Limited is hiring for Software Testing Engineer/ SDET
We re looking for a Software Development Engineer in Test who will design and enhance automation testing frameworks and automation test scripts using Python & Javascript.
Experience/ Required Skills
BS/ MS from a reputed institution in CS or equivalent engineering field. 3-9 years experience in software testing and test automation framework development. Strong QA mindset; should have an eye for potential gaps in a software application and be able to create thorough test plans. Excellent problem solving and programming skills as well as communication and collaboration skills. Ability to effectively work independently and as part of a team.
Strong programming experience (Python, JavaScript) in the context of the automation testing framework for UI/Rest API/Backend preferred.. Experience of testing Data Driven Applications, knowledge of databases, and knowledgeable on Data validation on variety of target platforms (HDFS/ DBFS/ Cloud Data Warehouse/RDBMs etc) preferred. Hands on experience on Cloud ecosystems (GCP/ AWS/ Azure). Familiarity with at least one of them is ideal. Experience of testing an application from both API and UI preferred.
Nice to Have
Prior experience of working in data engineering domain. Startup experience. Linux and shell scripting. Prior experience of using any test case management tool.
Job Location
Bangalore, India
Work Experience
3 - 9 Year/s
Educational Qualification
BCA/BCS, B.E./B.Tech, M.Tech, MCA/PGDCA
Required Skill Set
Automation Testing frameworks and Automation Test Scripts using Python & Javascript
About Us
Infoworks was founded to solve a common problem in the big data world. Big data is just too hard. Infoworks co-founder and Chief Product Officer, Amar Arsikere discovered this challenge first-hand at both Google and Zynga. He first built a data warehousing platform on Bigtable at Google, then built one of the worlds largest in memory database infrastructures at Zynga. And while open source tools were a good initial foundation, Amar found that out of the box open source didn`t translate well into production environments. They require too much expertise, too much manual coding, too much integration gluecode, and too much time to get big data infrastructure to work properly in production. To solve this problem, Amar created an automation layer to eliminate complexity across the entire data workflow. The solution included data ingestion, data synchronization, data preparation, cube generation and orchestration and management of data workflows in production on a collaborative platform.
