Position: Senior Manual Data QA engineer
Company: Soft Industry Alliance Ltd
Category: QA
Experience required: 5y
English level: fluent

Looking for a Middle+/Senior Manual Data QA engineer for long-term cooperation.

Project: SAP Finance
Team: 3 QA engineer, Product Owner, Scrum master, QA lead, 3 automation QAs, developers
Full remote, full-time, long-term project

We are searching for a hands-on tester that can help us test our SAP reporting requirements. 

Tester should be comfortable working with data and be able to seek out opportunities to validate the correctness of the data themselves.

**Requirements:**
1. Should have strong/in-depth knowledge on Finance Domain Processes and SAP modules (OTC/Invoicing/Accounting etc.).
It is not necessarily, all this can be learned in the process of work.
2. Experience in Data Science and Big Data is desirable.
3. Should be able to communicate with Product Owners/Developers on the functional requirements
4. Should have strong experience in writing manual test cases
5. Should be able to work cross products within Finance
6. Tosca automation knowledge is preferred but not mandatory
7. Should help QA team to execute functional test cases
8. Should be able to help automation engineers to understand process flow
9. More focus on HandsOn than Management or Lead activities.
10. Advanced level of English (work in international team).

**Soft skills:**
1. Strong in communication, analytical, and creative
2. Proactive person
3. Team player
4. Solution-oriented mindset

**Responsibilities:**
1. Manual testing of Web;
2. Review requirements documents;
3. Existing test documentation creation and support;
4. Striving for a detailed understanding of every detail of the product;
5. Active participation in the development process.
6. Identifies/creates the relevant acceptance/regression tests based on business requirements.
7. Validation of the reports functionally/ output with business requirements and mock-ups.
8. Regression testing including standard data validation and ensuring data maintains integrity at all stages of transformation (ETL) and data flow.
9. Data reconciliation in reports by means of reports comparison and by executing SQL queries. 
10. Validate the data through the ETL process, and interrogate the relevant databases using queries to ensure data integrity, consistency, and accuracy.
11. Identifying and delivering a reusable set of test scripts - focused on testing the migration and transformation of data.
12. Creating test artifacts (test completion reports) that can contain objectives, activities, and results.
13. Experience in testing in data-driven environments utilizing SQL skills.