Quality and Analytics Specialist

Quality and Analytics Specialist  About Us   We do things differently. We build a solution for enterprises to make sense of all of their information. We know how important it is for companies to understand their customers, so we provide our technology to solve their biggest challenges. We believe in open and transparent communication, not strict rules and hierarchies. We are a team of hardworking, talented people who aim to build software that makes sense of data.

We've got some huge challenges ahead of us, and we need smart, driven wordsmiths to help us tackle them. If you think you've got what it takes—join us.
Role Summary
We are looking for a Manual QA Engineer specializing in Data Quality and Analytics Testing. This role requires strong hands-on experience in manual testing, data validation, and backend verification, along with practical exposure to Jupyter notebooks, Python, and data libraries for validation purposes.
This is a QA role with heavy emphasis on manual testing and data validation, not a data engineering or data science position..

Key Responsibilities
Manual QA & Functional Testing
Perform end-to-end manual testing of UI/data and analytics features.
Validate application workflows, UI behavior, and backend data processing.
Design and execute detailed test cases, test scenarios, and regression suites.
Perform exploratory, negative, and boundary testing.
Pipeline & Platform Testing
Perform manual testing of Spark/PySpark data pipelines.
Validate ingestion from files, APIs, and cloud storage.
Execute sanity, regression, and volume testing for large datasets.
Notebook & Python-Based Validation
Test and validate Jupyter Lab / notebook-based workflows.
Execute and review notebooks for correctness, stability, and expected outputs.
Use Python and data libraries (Pandas, Num Py, PySpark) to support validation and comparisons (not for model development).
Validate analytical results and derived datasets.

Data Quality & Validation
Conduct source-to-target data validation using code and No code process.
Validate transformations, joins, aggregations, business rules, and calculations.
Identify data quality issues and inconsistencies with pipeline outputs.
Test Documentation & Defect Management
Create and maintain test plans, test cases, and validation checklists.
Log, track, and verify defects using Jira or similar tools.
Collaborate with engineering teams to reproduce and resolve data issues.
Required Skills
Must Have
3+ years of experience in Manual QA / Data QA / Backend Testing
Strong understanding of QA processes, SDLC, STLC, and defect life cycle
Excellent SQL for data validation and reconciliation
Practical exposure to Jupyter notebooks / Jupyter Lab
Experience using data libraries such as Pandas, Num Py, Py Spark

Experience with Jira or similar bug tracking tools
Good to Have
Basic test automation exposure

Experience with cloud platforms (AWS / GCP)
Exposure to Airflow or other workflow schedulers
Exposure to geospatial datasets/tools (Sedona / Geo Mesa)

Note:

This role requires strong experience in Manual QA and Data Quality testing. Candidates with only Data Engineering or Data Science backgrounds and no QA experience are not suitable.