Scan your resume against ATS criteria for this CSI Analytics - Synthetic Data Technical Analyst role at EY.
At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all.
The Synthetic Data Technical Analyst (Senior Associate) will be responsible for supporting the delivery, implementation, validation, and operation of Synthetic Data solutions across the organization. Working as part of the Synthetic Data Service team, the role will focus on generating, testing, validating, and maintaining Synthetic Data assets that support analytics, AI development, testing, performance testing, integration testing, business innovation, and secure data sharing.
The successful candidate will possess strong technical capabilities across Python development, SQL, Data Engineering, Machine Learning, Statistical Analysis, and Data Modelling disciplines. The role requires a hands-on technical practitioner responsible for analysing source datasets, implementing generation logic, validating outputs, and supporting the development of reusable Synthetic Data generation frameworks.
Working under the guidance of the Synthetic Data Technical Lead, the analyst will contribute to the delivery of Synthetic Data engagements from requirements gathering through implementation, validation, documentation, and support.
This Synthetic Data Technical Analyst will be responsible for delivering scalable, secure, and high quality synthetic data solutions to meet exceptional client and internal stakeholder expectations. Standard work includes but is not limited to:
Analyse source datasets, schemas, relationships, dimensions, distributions, and business rules required for Synthetic Data generation.
Develop and maintain Python, SQL, and automation scripts supporting Synthetic Data generation and validation.
Implement rule-based, statistical, simulation, and machine-learning-driven generation techniques.
Support the creation and maintenance of reusable generation blueprints and framework components.
Generate and validate Synthetic Data datasets for testing, analytics, AI, and business innovation use cases.
Support modelling of facts, dimensions, hierarchies, relationships, and business rules within Synthetic Data solutions.
Execute data profiling, statistical analysis, distribution testing, and validation activities.
Validate generated datasets to ensure appropriate levels of data utility, statistical fidelity, business realism, privacy protection, and business-rule compliance.
Troubleshoot generation issues, data quality problems, and validation exceptions.
Support development of reusable validation frameworks and automation assets.
Support governance processes to ensure Synthetic Data solutions comply with privacy, security, and enterprise standards.
Assist with disclosure risk assessments, validation reporting, and governance documentation.
Maintain technical documentation, delivery artefacts, audit trails, and supporting evidence.
Ensure all activities align with organizational data governance and compliance requirements.
Support day-to-day operation of the Synthetic Data Service.
Assist with user support requests, issue investigations, and delivery activities.
Contribute to reusable assets, templates, accelerators, and best practices.
Participate in knowledge-sharing initiatives and continuous improvement activities.
Stay current with emerging Synthetic Data technologies, methodologies, and industry trends.
The role may require periodic allocation of additional time to support critical deliveries, operational activities, or collaboration with global teams operating across multiple time zones.
Bachelor's degree in Computer Science, Data Science, Engineering, Information Systems, Mathematics, Statistics, or a related discipline.
Relevant Certifications in data Engineering, Analytics, Cloud Technologies, AI, or Synthetic Data Technologies are advantageous.
Synthetic Data Engineering Expertise (Mandatory) – Minimum 3+ Years Practical Experience
Candidates must demonstrate practical experience supporting the implementation, operation, or delivery of Synthetic Data, Test Data Management, Data Engineering, Data Analytics, Machine Learning, Data Simulation, or Data Modelling solutions within enterprise environments.
The successful candidate should have experience in:
Python development.
SQL development.
Data profiling and analysis.
Data quality assessment and validation.
Statistical analysis and distributions.
Business rule implementation.
Synthetic Data generation techniques.
Data modelling and schema analysis.
Machine Learning fundamentals.
Experience working within Data Engineering, Data Analytics, Data Management, AI, or related technical disciplines.
Experience working with structured and unstructured datasets.
Experience supporting data transformation, integration, and automation activities.
Strong understanding of data quality and data governance principles.
Python
SQL
Data Profiling
Data Quality & Validation
Synthetic Data Generation Techniques
Statistical Analysis
Data Modelling
Machine Learning
Azure Data Services
Data Engineering
APIs & Automation
Git & DevOps
Data Governance
Metadata Management
Candidates must demonstrate practical Synthetic Data engineering experience using Python, SQL, statistical modelling, machine learning, business-rule implementation, data modelling, and validation techniques. Experience with commercial Synthetic Data platforms is advantageous but not mandatory.
Teamwork
Integrity
Empathy & care
Ownership
Accountability
Coherent communication
Time management and organization
Extreme attention to detail
Built or maintained Python and SQL generation logic.
Analyzed source schemas, relationships, dimensions, and business rules.
Generated or validated Synthetic Data outputs.
Performed data quality, statistical, and validation activities.
Supported delivery of Synthetic Data, Test Data, Analytics, or AI solutions into operational use cases.
Synthetic Data generation techniques used.
Python and SQL development activities performed.
Validation and quality assessment approaches.
Business rule implementation.
Technical challenges encountered and lessons learned.
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.
How to apply for CSI Analytics - Synthetic Data Technical Analyst at EY?
Click the "Apply on Company Website" button on this page to submit your application directly on the employer's official portal.
What is the salary for this role?
Salary details will be discussed during the interview.
What experience is required?
This position is open to freshers and experienced candidates.
Is this position still open?
Yes, currently active and accepting applications.
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