Position: Data Automation QA Engineer
Company: Chimplie
Experience required: 2y
Category: QA Automation
English level: upper
Published: 2022-01-01T00:00:00+02:00

We are developing tools that enable millions of students and young professionals to reflect upon their career path, to express their expectations towards employers, and to connect with the ones who match them best. At the same time, we build instruments for data processing, presentation, analytics, and reporting. The platform is used by the world's largest companies to understand the ever-changing career preferences and ambitions of their current and future employees.

We have a microservices architecture built with Django, Flask, FastAPI, and Peewee. The data is stored in PostgreSQL and AWS DynamoDB. On the front-end we use React (Typescript) with Redux. The infrastructure is powered by AWS ECS, S3, SQS, and Lambdas.

This is primarily a technical QA position, where you will have an opportunity to work with back-end engineering, databases, and data processing. Pandas, PostgreSQL, and AWS will be among your main instruments.

You will overlook how our platform performs data processing, ensuring correct output. Day to day tasks will range between automating API tests, writing scripts to extract and process data, and supporting business stakeholders with various data insights.

**Key requirements:**
- Effortless English
- General understanding of microservices, APIs, and databases
- Experience with programming (ideally Python)
- Experience with API test automation
- Experience implementing QA processes, knowledge of modern QA practices

**Bonus (nice-to-haves):**
- Product team experience
- Experience testing data collection and processing systems
- Experience as solo QA engineer in a cross-functional team
- Experience with Pandas
- Experience with AWS

**Team set-up:**
There are 18 people in the Engineering department, which is broken-down into 4 cross-functional teams. Half of the team are Senior engineers with 10+ years of experience, with the other half ranging from 2-10 years.

**Our interview process:**
0. Intro call (10 mins)
1. Soft evaluation (45 mins)
2. Technical evaluation (1 hour)
3. Final call with CTO