At a Glance
- Tasks: Lead quality engineering for data pipelines, ensuring robust and reliable data delivery.
- Company: Join a fast-paced, data-driven organisation making impactful decisions.
- Benefits: Competitive salary, flexible working, and hands-on experience with modern data technologies.
- Other info: Opportunity to mentor junior testers and grow in a dynamic environment.
- Why this job: Make a real difference in data accuracy and quality while shaping testing practices.
- Qualifications: Experience in ETL testing, SQL, Python automation, and API testing required.
The predicted salary is between 60000 - 80000 £ per year.
This is a greenfield opportunity to establish and lead the quality engineering approach for business-critical data pipelines (ETL/ELT) within a fast-moving, data-driven environment. The organisation operates in an industry where data accuracy, consistency, and timeliness are essential to analytics, reporting, and downstream systems, making this role highly impactful.
You will work on end-to-end data projects, covering data ingestion, transformation, and delivery into outputs such as APIs, Excel reports, and XML files. Your role will ensure that these pipelines are robust, reliable, and production-ready, enabling confident business decision-making.
The position provides hands-on access to modern data technologies, including Databricks for large-scale data processing, Python-based automation (Pytest, Pandas), structured validation tools (OpenPyXL, lxml, xmlschema), and CI/CD implementation using GitHub Actions. You will define best practices and build scalable automation frameworks, rather than inherit existing processes.
Overall, you will play a key role in building trust in data, reducing manual validation effort, and enabling faster, high-quality data delivery across the organisation.
Your responsibilities:
- Take full ownership of data pipeline testing strategy, defining scope, priorities, and standards.
- Design and implement a scalable Pytest-based automation framework for ETL/data validation.
- Develop robust SQL-based validation checks (reconciliation, duplicates, nulls, business rules).
- Automate validation of Excel outputs using Pandas and OpenPyXL, ensuring structural and data accuracy.
- Validate XML outputs using lxml and xmlschema, including schema compliance and business-level rules.
- Own and automate API testing workflows using Postman and Newman.
- Integrate all testing workflows into GitHub Actions, enabling CI/CD-driven quality gates.
- Build clear, actionable reporting and logging, making failures easy to diagnose and debug.
- Collaborate closely with data engineers to improve testability, catch defects early, and accelerate resolution.
- Mentor and guide junior testers, establishing best practices, code standards, and teamwork flows.
Your Profile
Essential skills/knowledge/experience:
- Strong hands-on experience in testing data pipelines / ETL/ELT systems in complex data environments.
- Advanced SQL skills, with the ability to write complex queries for data validation, debugging, and reconciliation.
- Proven expertise in test automation using Python with Pytest, including building reusable frameworks (fixtures, utilities, modular design).
- Experience using Pandas and OpenPyXL for validating data outputs such as Excel files (structure, values, comparisons).
- Hands-on experience validating XML data using lxml and xmlschema, including schema (XSD) validation and business rules.
- Solid experience in API testing using Postman and automation using Newman.
- Practical experience implementing CI/CD pipelines using GitHub Actions for automated test execution and quality gates.
- Familiarity with Databricks or similar modern data platforms for large-scale data processing and validation.
- Strong understanding of data quality principles, including data integrity, transformation validation, and reconciliation techniques.
- Experience in building QA processes from scratch or leading data testing initiatives, with ability to define standards and mentor others.
Desirable skills/knowledge/experience:
- Exposure to modern data stacks / cloud environments supporting ETL/ELT pipelines.
- Experience with data quality tools (e.g., Great Expectations).
- Knowledge of data orchestration tools (e.g., Airflow, ADF).
- Experience in performance and large-volume data testing.
- Familiarity with end-to-end data validation across APIs, files (Excel/XML), and pipelines.
- Prior experience in setting up or scaling QA practices for data platforms.
Automation Tester – Data Pipelines (ETL) in Stevenage employer: Tcs Uk
As a Data Governance Specialist at our company, you will be part of a dynamic team dedicated to driving customer excellence through robust data governance practices. We pride ourselves on fostering a collaborative work culture that values innovation and continuous learning, offering ample opportunities for professional growth in a supportive environment. Located in a vibrant area, our workplace not only provides a stimulating atmosphere but also encourages a healthy work-life balance, making it an ideal place for those seeking meaningful and rewarding employment.
StudySmarter Expert Advice🤫
We think this is how you could land Automation Tester – Data Pipelines (ETL) in Stevenage
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We think you need these skills to ace Automation Tester – Data Pipelines (ETL) in Stevenage
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Tcs Uk, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Tcs Uk. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Tcs Uk
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Tcs Uk!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
