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As a Synthetic Data Technical Analyst (Senior Associate) within the CSI Analytics team, you will be instrumental in supporting the delivery, implementation, validation, and operation of Synthetic Data solutions across the organization. You will be part of the dedicated Synthetic Data Service team, focusing on generating, testing, validating, and maintaining synthetic data assets. These assets are vital for analytics, AI development, testing, performance and integration testing, business innovation, and secure data sharing.

This role demands strong technical expertise in Python development, SQL, Data Engineering, Machine Learning, Statistical Analysis, and Data Modelling. As a hands-on technical practitioner, you will analyze source datasets, implement generation logic, validate outputs, and contribute to the development of reusable synthetic data generation frameworks. Working under the guidance of the Synthetic Data Technical Lead, you will contribute to the entire lifecycle of synthetic data engagements, from requirements gathering to implementation, validation, documentation, and ongoing support.

Deliver scalable, secure, and high-quality synthetic data solutions that exceed client and internal stakeholder expectations. Key responsibilities include:

- Analyze source datasets, schemas, relationships, dimensions, distributions, and business rules essential for synthetic data generation. - Develop and maintain Python, SQL, and automation scripts to support synthetic data generation and validation processes. - Implement various generation techniques, including rule-based, statistical, simulation, and machine-learning-driven approaches. - Contribute to the creation and maintenance of reusable generation blueprints and framework components. - Generate and validate synthetic data datasets for diverse use cases such as testing, analytics, AI, and business innovation.

Engage in synthetic data engineering and validation, including: - Support the modelling of facts, dimensions, hierarchies, relationships, and business rules within synthetic data solutions. - Execute data profiling, statistical analysis, distribution testing, and comprehensive validation activities. - Validate generated datasets to ensure optimal data utility, statistical fidelity, business realism, privacy protection, and business-rule compliance. - Troubleshoot generation issues, data quality problems, and validation exceptions promptly. - Assist in the development of reusable validation frameworks and automation assets.

Uphold governance, documentation, and compliance standards by: - Supporting governance processes to ensure synthetic data solutions adhere to privacy, security, and enterprise standards. - Assisting with disclosure risk assessments, validation reporting, and governance documentation. - Maintaining accurate technical documentation, delivery artifacts, audit trails, and supporting evidence. - Ensuring all activities align with organizational data governance and compliance requirements.

Contribute to service support and continuous improvement by: - Supporting the day-to-day operations of the Synthetic Data Service. - Assisting with user support requests, issue investigations, and ongoing delivery activities. - Contributing to reusable assets, templates, accelerators, and best practices. - Participating in knowledge-sharing initiatives and continuous improvement efforts. - Staying abreast of emerging synthetic data technologies, methodologies, and industry trends.

To excel in this role, you will need a Bachelor's degree in Computer Science, Data Science, Engineering, Information Systems, Mathematics, Statistics, or a closely related field. Relevant certifications in data engineering, analytics, cloud technologies, AI, or synthetic data technologies are considered advantageous.

You must possess a minimum of 3 years of practical experience in synthetic data engineering. This includes demonstrable 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.

Key experience areas include: - Proficient Python development. - Strong SQL development skills. - Experience with data profiling and analysis. - Expertise in data quality assessment and validation. - Solid understanding of statistical analysis and distributions. - Ability to implement business rules effectively. - Practical experience with synthetic data generation techniques. - Familiarity with data modelling and schema analysis. - Fundamentals of machine learning.

Furthermore, you should have experience working within Data Engineering, Data Analytics, Data Management, AI, or related technical disciplines. Experience with both structured and unstructured datasets, alongside supporting data transformation, integration, and automation activities, is expected. A strong understanding of data quality and data governance principles is essential.

Candidates must be able to demonstrate at least two enterprise-scale synthetic data implementations where they have personally: - 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 the delivery of synthetic data, test data, analytics, or AI solutions into operational use cases.

Be prepared to discuss specific synthetic data generation techniques used, Python and SQL development activities performed, validation and quality assessment approaches, business rule implementation, and any technical challenges encountered along with lessons learned. Experience limited to vendor demonstrations, academic research, training courses, proof-of-concepts, or theoretical knowledge will not be considered sufficient.