At EY, we're all in to shape your future with confidence. 

 

We'll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. 

 

Join EY and help to build a better working world. 

 

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 Quality Engineer to join our Transformation Realized™ Consulting practice. Our team is part of EY's Central, Eastern and Southeastern Europe & Central Asia (CESA) cluster, delivering market leading services to organizations across industries in Cyprus and internationally. 

 

The transformation imperative is urgent, challenging and opportunity-rich, interested to join us? 

 

Your key responsibilities 

 

Data Profiling & Analysis 

 

Execute comprehensive data profiling on source systems to identify data quality issues, patterns, and anomalies 

Analyze large-scale datasets (millions of records) using statistical techniques for distributions, null rates, and outliers 

Profile customer data to support deduplication and data matching requirements 

Document data quality findings and contribute to baseline metrics reporting 

Conduct cross-system data comparison to identify overlaps, conflicts, and data inconsistencies 

Perform root cause analysis for data quality failures using systematic methodologies 

Create Pareto charts and defect distribution analysis to support prioritization of remediation efforts 

 

 

Data Quality Rules & Validation 

 

Implement and maintain data quality rules across multiple business domains based on defined requirements 

Develop validation logic for business-specific rules including calculations, limits, and regulatory requirements 

Configure referential integrity checks across related data entities 

Build lookup validation rules to prevent mapping and definition mismatches 

Execute attribute-level validation to support high success rates with regression detection 

Test and validate data quality rules in development and test environments before production deployment 

 

Data Cleansing & Transformation 

 

Implement data cleansing rules for standardization, normalization, and enrichment based on specifications 

Apply address standardization and validation rules using industry-standard references 

Execute name parsing and normalization processes for improved data matching accuracy 

Support engineering teams with ETL transformation logic and data mapping validation 

Implement data quality checkpoints within data pipelines (pre-transformation, post-transformation, pre-load) 

Validate that exception handling and error routing mechanisms work correctly for data quality failures 

Support customer deduplication processes with data quality validation for matching and merge operations 

 

Data Quality Monitoring & Reporting 

 

Execute real-time data quality monitoring across all data processing stages 

Develop and maintain automated workflows for continuous data quality validation using orchestration tools 

Configure alerting mechanisms for data quality threshold violations and degradation patterns 

Build and maintain data quality dashboards using PowerBI or Tableau for stakeholder visibility 

Track comprehensive data quality metrics including attribute success rates, volumetric reconciliation, and financial accuracy 

Create technical dashboards with drill-down capabilities for root cause investigation 

Contribute to trend analysis visualizations to support regression pattern detection 

Support daily and weekly data quality reporting for technical and business stakeholders 

 

Documentation & Data Lineage 

 

Document data quality test cases, validation procedures, and testing results 

Maintain data quality runbooks for issue resolution and troubleshooting 

Support data lineage documentation showing transformation points and validation checkpoints 

Contribute to data quality assessment reports for stakeholder review 

Update lessons learned repository with data quality insights from testing activities 

Maintain up-to-date documentation of data quality rules, validation logic, and test coverage 

 

Collaboration & Stakeholder Management 

 

Collaborate with test automation engineers on data validation strategies 

Work closely with data architects and ETL developers to understand data flows and transformation logic 

Partner with business analysts to translate business requirements into data quality validation rules 

Participate in defect triage meetings and provide data quality analysis 

Present data quality findings to technical and business stakeholders 

Support UAT activities by providing data quality insights to business subject matter experts 

Ensure clear and consistent communication with all stakeholders throughout the data quality lifecycle 

 

Skills and attributes for success 

 

Experience & Education 

 

4-7 years of hands-on experience in data quality engineering or data analysis 

Experience in large-scale data migration programs with millions of records 

Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, or related field (preferred) 

 

Data Quality Expertise 

 

Strong understanding of data quality dimensions: completeness, accuracy, consistency, validity, timeliness, uniqueness 

Experience designing and implementing data quality frameworks and validation rules 

Proficiency in data profiling techniques and statistical analysis 

Knowledge of data cleansing, standardization, and normalization methodologies 

Experience with data reconciliation frameworks (volumetric, financial, attribute-level) 

 

Technical Skills 

 

Advanced SQL skills for complex data validation queries across multiple databases 

Proficiency in Python for data quality automation such as pandas, PyTest, sqlalchemy 

Experience with data quality tools such as Great Expectations, PyDeequ, or enterprise DQ platforms (e.g. Informatica, Talend) 

Knowledge of data warehouse platforms such as Snowflake, Databricks, Redshift 

Experience with cloud technologies such as AWS, Azure, GCP for data processing 

Familiarity with ETL/ELT tools (AWS Glue, Apache Airflow, Databricks) 

Version control with Git and CI/CD pipeline integration 

 

 

Data Analysis & Visualization 

 

Experience creating dashboards and visualizations using PowerBI, Tableau, or similar tools 

Strong analytical skills to identify patterns, trends, and anomalies in large datasets 

Ability to perform statistical analysis and create meaningful metrics and KPIs 

Experience with data visualization best practices for technical and executive audiences 

 

Methodologies & Processes 

 

Solid understanding of the software development lifecycle (SDLC) and Agile methodologies 

Experience with data governance principles and frameworks 

Knowledge of regulatory compliance requirements (e.g. data protection standards) 

Root cause analysis and problem-solving methodologies 

Strong interest in continuous improvement and lessons learned application 

 

Soft Skills & Work Style 

 

Strong problem-solving skills and attention to detail 

Excellent command of English, both written and spoken 

Ability to communicate complex technical concepts to non-technical stakeholders 

Self-driven and flexible, can work autonomously with proven work ethic 

Team player who enjoys working with people from different backgrounds and disciplines 

Ability to work in a dynamic environment with excellent organizational and time management skills 

Able to exhibit a high level of confidentiality 

 

It will be a plus if you have: 

 

Domain Knowledge 

 

Experience in financial services, insurance, banking, or highly regulated industries 

Understanding of insurance policy lifecycle and claims workflows 

Knowledge of customer data management and master data management (MDM) principles 

Familiarity with regulatory requirements (e.g. PCI-DSS) 

 

Advanced Technical Capabilities 

 

Experience with PySpark for large-scale data processing 

Knowledge of machine learning techniques for data quality improvement (anomaly detection, predictive quality) 

Experience with Docker and Kubernetes for containerized data quality processes 

Familiarity with data masking, anonymization, and synthetic data generation 

Knowledge of Infrastructure as Code tools (Terraform, CloudFormation) 

 

Data Quality Tools & Platforms 

 

Hands-on experience with enterprise data quality platforms (e.g. Informatica DQ, Talend) 

Experience with open-source data quality frameworks (e.g. Great Expectations, Deequ, Soda) 

Knowledge of data catalog tools (e.g. Collibra, Alation, Apache Atlas) 

Experience with data observability platforms (e.g. Monte Carlo, Datadog) 

 

Migration & Transformation Experience 

 

Previous involvement in large-scale data migration programs (1M+ records) 

Experience with merger and acquisition data integration projects 

Understanding of customer deduplication and entity resolution challenges 

Knowledge of legacy system modernization and cloud migration patterns 

 

Certifications 

 

Data quality or data management certifications such as CDMP, DGSP 

Cloud certifications such as AWS Certified Data Analytics, Azure Data Engineer, GCP Data Engineer 

Snowflake or Databricks certifications 

ISTQB or software testing certifications 

 

What we offer you 

 

At EY, we'll develop you with future-focused skills and equip you with world-class experiences. We'll empower you in a flexible environment, and fuel you and your extraordinary talents in a diverse and inclusive culture of globally connected teams. Learn more. 

 

In addition to a competitive salary, our benefits include but are not limited to: 

 

13th salary 

Provident Fund 

Private Medical and Life Insurance 

Flexible working arrangements (hybrid work and flexible work schedule) 

Friday afternoon off 

EY Tech MBA and EY MSc in Business Analytics 

EY Badges - digital learning certificates 

Mobility programs (if interested to work abroad) 

Paid Sick Leave 

Paid Paternity Leave 

Yearly wellbeing days off 

Maternity, Wedding and New Baby Gifts 

EY Employee Assistance Program (EAP) (counselling, legal and financial consultation services) 

 

About EY 

 

EY | Building a better working world 

 

EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. 

 

Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform, and operate. 

 

Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today. 

 

#betterworkingworld 

 

If you can demonstrate that you meet the criteria above, please contact us as soon as possible. 

 

The exceptional EY experience. It's yours to build.