The opportunity  

Our EY Consulting ambition is to become the world’s leading transformation consultants, trusted to help our clients generate long-term value. We’re building world-class capabilities in business, technology and people consulting to help us deliver on EY’s purpose of building a better working world - our firm’s broader ambition to become the world’s most trusted, distinctive professional services organization. 

Our clients are at the heart of our new strategy. We’re focused on solving the key issues of our client buyers, building deeper relationships, and making a greater impact. We’re introducing a new go-to-market narrative - Transformation Realized™ - to help us harness the core drivers of transformation that will create long-term value for our clients.  

To achieve this, we are seeking a Data QA Engineer to join our Transformation Realized™ Consulting practice to support our clients in ensuring the reliability, accuracy, and integrity of their data platforms, data pipelines, and analytics solutions. Our team is part of EY’s Europe Central (EC) cluster, delivering market leading services to organizations across industries in Cyprus and internationally.   

Key Responsibilities

• Design and implement data quality validation frameworks across data platforms and pipelines

• Validate ETL/ELT processes, ensuring correctness of data transformations and business rules

• Perform source-to-target data validation and reconciliation across systems

• Define and execute automated data quality checks (e.g., completeness, accuracy, consistency, timeliness)

• Identify and investigate data anomalies, such as missing, duplicate, or inconsistent data

• Collaborate with data engineers, analysts, and business stakeholders to define validation rules and acceptance criteria

• Support testing of data pipelines and data integration processes across modern data architectures

• Perform root cause analysis for data issues and support remediation efforts

• Contribute to data quality standards, governance, and best practices across engagements

• Document validation logic, test scenarios, and data quality processes 

Skills and Attributes for Success

• Bachelor’s and/or Master’s degree in Computer Science, Engineering, Mathematics, or a related field

• 2–5 years of experience in data engineering, data QA, or data testing

• Strong proficiency in SQL (core requirement)

• Experience testing data pipelines (ETL/ELT) and transformation logic

• Good understanding of data modelling concepts (fact/dimension models, SCDs)

• Familiarity with data warehousing and modern data architectures

• Experience working with large datasets and validating data at scale

• Exposure to cloud environments (Azure, AWS, or GCP)

• Strong analytical thinking and problem-solving skills

• Ability to work with structured and unstructured data

• Effective communication and stakeholder management skills

It will be a plus if you have  

• Experience with data quality tools/frameworks (e.g., Great Expectations, dbt tests, Deequ, or similar)

• Programming skills in Python (for validation and automation)

• Experience with data pipeline orchestration tools (e.g., Airflow, Azure Data Factory, or similar)

• Exposure to CI/CD practices for data platforms

• Understanding of data governance and metadata management tools (e.g., Purview, Collibra, Informatica, or similar)

• Experience in Financial Services (Banking, Insurance)

• Familiarity with agile/lean development methodologies

This position plays a key role in ensuring the trustworthiness of data used for reporting, analytics, and decision-making, working closely with data engineering teams to embed quality across the data lifecycle.