Overview

CSI Analytics – Synthetic Data Technical Lead

Responsibilities

• Define and execute the Synthetic Data Service strategy, roadmap, operating model, governance framework, and adoption approach.
• Establish service offerings, delivery methodologies, engagement models, support processes, and success measures.
• Drive enterprise adoption of Synthetic Data by identifying opportunities, demonstrating value, and educating stakeholders on appropriate use cases and benefits.
• Act as the primary point of contact and trusted advisor for Synthetic Data initiatives across business and technology teams.
• Continuously evaluate emerging market capabilities, industry trends, and new technologies to enhance the Synthetic Data service.
• Lead the design and delivery of Synthetic Data solutions using Python, SQL, statistical modelling, rule-based generation, simulation techniques, supervised and unsupervised machine learning models.
• Define reusable generation blueprints, business rule frameworks, schema patterns, validation controls, and automation standards.
• Lead the modelling of facts, dimensions, relationships, distributions, hierarchies, and business logic required to generate realistic Synthetic Data.
• Review and approve generation logic, Python code, validation methodologies, machine learning approaches, and delivery artefacts.
• Drive the evolution of the capability from manually engineered datasets toward reusable frameworks, Azure-based automation, API enablement, and future AI-assisted generation services.
• Establish and maintain governance processes that ensure Synthetic Data solutions meet organizational, legal, regulatory, and security requirements.
• Define standards and controls covering privacy protection, disclosure risk, data quality, model governance, auditability, and responsible AI practices.
• Lead the assessment and validation of Synthetic Data outputs to ensure appropriate levels of data utility, statistical fidelity, business realism, and privacy protection.
• Work closely with Data Governance, Information Security, Risk, Legal, and Privacy teams to obtain approvals and ensure compliance.
• Ensure all Synthetic Data solutions are fully documented, traceable, auditable, and aligned to enterprise governance standards.
• Own the operational performance and continuous improvement of the Synthetic Data service.
• Establish service metrics, KPIs, reporting, support processes, and operational controls.
• Manage demand intake, prioritization, resource planning, and delivery governance across multiple engagements.
• Monitor service adoption, customer satisfaction, delivery quality, and business outcomes.
• Support the evolution of the capability from a managed service model toward broader platform ownership, automation, and self-service capabilities.
• Lead, mentor, and develop a team of Synthetic Data Technical Analysts.
• Foster a culture of technical excellence, innovation, collaboration, and continuous learning.
• Support recruitment, performance management, career development, and capability building within the team.
• Provide coaching on Synthetic Data methodologies, technical delivery, stakeholder engagement, and governance practices.
• Promote knowledge sharing and establish a community of practice for Synthetic Data across the organization.

Qualifications

• Bachelor’s degree in Computer Science, Data Science, Engineering, Information Systems, Mathematics, Statistics, or a related discipline.
• Master's degree or relevant specialist certifications are advantageous.
• Synthetic Data Engineering Expertise (Mandatory) – Minimum 7+ Years Practical Experience
• Candidates must demonstrate significant practical experience designing, building, validating, and governing Synthetic Data solutions within enterprise environments.
• Python and SQL development.
• Data modelling, schema design, and relationship modelling.
• Statistical modelling and distribution analysis.
• Data profiling and data quality assessment.
• Synthetic Data generation methodologies.
• Business rule engineering and rule-based generation.
• Machine Learning and AI concepts.
• Synthetic Data validation and testing.Data privacy, governance, and disclosure risk management.
• Azure-based data engineering and automation solutions.
• Python
• SQL
• Synthetic Data Generation Techniques
• Data Modelling
• Statistical Modelling
• Data Quality & Validation
• Data Privacy & Governance
• Technical Architecture
• Stakeholder Management
• Machine Learning
• Azure Data Services
• Data Engineering
• API Integration
• DevOps & Automation
• Metadata Management
• Data Product Management
• Teamwork