QA Automation Engineer

A rapidly growing consumer intelligence platform delivering insights for global brands is hiring a QA Automation Engineer.

Our platform synthesizes massive-scale data (billions of Google searches, social conversations, product reviews, and videos) to deliver actionable consumer insights for leading global brands such as Coca-Cola, Unilever, and Bayer.

Your Mission

We are seeking a QA Automation Engineer. You will be responsible for designing, developing, and maintaining automated testing frameworks that ensure the quality and reliability of our data pipelines, dashboards, and web applications.

Our platform processes data from multiple sources, through Azure Databricks pipelines, and presents insights via dashboards embedded in ASP.NET and React web applications. You will play a critical role in validating data integrity across this entire flow and automating operational processes.

Key Responsibilities

Test Automation Development

• Design, develop, and maintain automated test frameworks using Python and pytest

• Create end-to-end test suites for validating data pipelines from ingestion through visualization

• Implement browser automation using Playwright for testing Power BI and React dashboards, ASP.NET, and React web applications

• Build API test suites to validate data provider integrations and internal services

• Develop data validation tests comparing Web and Azure Databricks outputs against Azure Databricks and SQL Server production databases

Dashboard & Reporting Validation

• Automate verification of Power BI/React dashboard KPIs against source data

• Implement visual regression testing for dashboard layouts and components

• Validate user-facing metrics in ASP.NET and React applications match database values

• Test dashboard performance and load times under various conditions

Process Automation

• Automate repetitive operational tasks related to data pipeline management

• Build scheduled jobs for recurring QA checks

• Create self-healing automation that handles common failure scenarios

• Develop monitoring scripts that proactively identify pipeline issues

CI/CD & Infrastructure

• Integrate automated tests into CI/CD pipelines

• Configure test environments and manage test data

• Implement parallel test execution for faster feedback cycles

• Maintain test reporting dashboards using Allure or similar tools

Collaboration & Documentation

• Work closely with data engineers to understand pipeline logic and identify test scenarios

• Collaborate with front-end developers on dashboard testing strategies

• Create and maintain technical documentation for test frameworks and processes

• Participate in code reviews and contribute to engineering best practices

• Report defects through Jira with detailed reproduction steps and evidence

Required Qualifications

Technical Skills

Programming: 2+ years of Python development experience; clean, maintainable code

Test Automation: 1+ years building automated test frameworks; pytest expertise

SQL: SQL skills; experience with SQL Server and/or cloud data warehouses

Browser Automation: Expertise with Playwright

API Testing: Proficiency in REST API testing and validation

Version Control: Git proficiency; branching strategies, pull requests

CI/CD: Experience integrating tests into CI/CD pipelines

Database & Data Skills

• Understanding of data warehousing concepts and ETL/ELT processes

• Experience validating data transformations and business logic

• Familiarity with data quality dimensions (accuracy, completeness, timeliness, consistency)

Web Technologies

• Understanding of web application architecture (front-end/back-end)

• Experience testing Single Page Applications (React)

• Knowledge of HTML/CSS/JavaScript for element identification and validation

• Familiarity with headless browser developer tools for debugging

Preferred Qualifications

Highly Desirable

• Experience with Azure Databricks or Apache Spark

• Familiarity with Power BI or other BI tools

• Knowledge of Azure cloud services (Functions, Data Factory, Key Vault, DevOps)

• Experience with ASP.NET or .NET ecosystem

• Background in data engineering or analytics

Nice to Have

• Experience with performance testing (Locust, k6, or similar)

• Knowledge of containerization (Docker) for test environments

• Familiarity with infrastructure-as-code (Terraform, ARM templates)

• Experience with test management tools (Xray, Zephyr, TestRail)

• Understanding of machine learning pipelines and model validation

• ISTQB or similar QA certification

Technical Environment

You will work with the following technology stack:

Processing: Azure Databricks (PySpark), Custom ML Models

Storage: SQL Server, Azure Blob Storage

Visualization: Power BI (embedded dashboards), Apache Superset

Web Applications: ASP.NET (C#), React (JavaScript/TypeScript)

Cloud Platform: Microsoft Azure

Automation Stack You Will Build & Maintain:

Language: Python 3.11+

Test Framework: pytest

Browser Automation: Playwright

API Testing: httpx / requests

Database Clients: pyodbc, databricks-sql-connector

Data Manipulation: pandas

Configuration: YAML + Pydantic

Scheduling: Azure Functions / APScheduler

Reporting: Allure, pytest-html

Success Metrics

You will be measured on:

Test Coverage: Automated tests cover critical data paths and dashboard KPIs

Defect Detection: Bugs caught in automation before reaching production

Pipeline Reliability: Reduction in data quality incidents

Automation ROI: Manual testing hours saved through automation

Test Stability: Low flaky test rate (<5%); high first-pass success

Execution Speed: Test suite runs complete within CI time budgets

Growth Opportunities

This role offers paths to:

• Senior QA Automation Engineer – Lead automation architecture decisions

• QA Lead – Manage QA team and strategy

• SDET / Software Engineer in Test – Deeper integration with development

• Data Quality Engineer – Specialize in data pipeline quality

• DevOps/Platform Engineer – Focus on CI/CD and infrastructure

What We Offer

• Long-term B2B opportunity

• Fully remote

• Modern tech stack with latest tools and frameworks

• Collaborative engineering culture with knowledge sharing

• Opportunity to shape QA practices from the ground up

• Work with interesting data from multiple industries