EY Data Quality Engineer 

Cyprus, Nicosia 

889137646

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 totrend analysis visualizations to support regression pattern detection

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

• Data quality or data management certifications suchasCDMP, DGSP

• Cloud certifications suchasAWS Certified Data Analytics, Azure Data Engineer, GCP Data Engineer

• Snowflake or Databricks certifications

• ISTQB or software testing certifications