Data Engineer

• Build and maintain scalable data pipelines, models and serving layers across cloud data platforms.
• Define clean analytical schemas and semantic contracts for product, dashboard and AI consumption.
• Implement ELT/ETL pipelines using SQL, Python, DBT and modern orchestration patterns.
• Design data models that support tenant/client separation, permission-aware access and audit requirements from the ground up.
• Apply lakehouse/medallion patterns (bronze/silver/gold) where they earn their place, avoiding architectural complexity that does not serve a clear use case.
• Build quality checks, reconciliation controls and monitoring for data freshness, completeness and accuracy.
• Instrument pipelines with logging, metrics and alerting so that failures are detected and triaged quickly.
• Define and maintain SLAs/SLOs for critical datasets in collaboration with product and business owners.
• Own incident response and post-incident learning for the data domains assigned to the role.
• Integrate operational systems, finance systems, CRM systems, external APIs and reporting platforms where needed.
• Help onboard acquired businesses onto the Group's data platform with a repeatable, documented pattern rather than bespoke effort each time.
• Work with AI/ML engineers to expose approved data functions without leaking raw platform details or unauthorized data access.
• Support embedded analytics and native dashboard development by providing governed metric definitions and performant data views.
• Champion CI/CD, code review, documentation, naming standards and data governance practices.
• Mentor less experienced engineers and help set the quality bar for data engineering delivery.
• Contribute to platform decisions: tooling selection, modelling conventions, lineage approach and metric definition processes.
• Drive a culture of "build it once, use it many times" so that delivery teams benefit from clear source mappings, metric definitions and data contracts.

Senior Data Engineer

• Lead the data migration Analysis, create Migration Strategy and Plan
• Work closely with Data owners, Business Owners, Architects, Application teams and Integration lead to understand the requirements and define the data migration strategy
• Assess the build efforts and propose the most cost effective, fastest and reusable data migration model which can be implemented at regional level
• Manage the entire data migration delivery from source and target side
• Articulate the problem statement and propose effective solution and recommendation for management decision
• Provide progress updates and highlight the risk and issues of the project to the key stakeholders and project/program manager
• Manage the scope and schedule of Data migration project
• Work with other architects to ensure that all components work together to meet objectives and performance goals as defined in the requirements
• Identify and communicate any cross-area or cross-release issues that may affect other areas of the project
• Demonstrate the ability to achieve high level of data integrity, accuracy, security & quality by applying proven Data Mapping, Validation, Cleansing, Reconciliation and Test strategies.

Data Migration Lead

• Work on data migration analysis, create migration strategy and plan.
• Collaborate with Data owners, Business Owners, Architects, Application teams, and Integration lead to understand requirements.
• Manage data migration delivery from source and target side.
• Articulate problem statements and propose effective solutions and recommendations for management decisions.
• Provide progress updates and highlight risks and issues to key stakeholders and project managers.
• Manage the scope and schedule of the data migration project.

Data Migration Engineer

• Design, build, and maintain scalable, fault-tolerant data pipelines (batch and/or streaming) for core business and product data.
• Ingest data from diverse sources including APIs, databases, event streams, and third-party services, ensuring high data quality and reliability.
• Design and manage data models and storage layers (data warehouses, data lakes) that support analytics and downstream use cases.
• Partner with analytics, product, and engineering teams to deliver clean, well-documented datasets that enable self-service analytics and experimentation.
• Implement monitoring, logging, and alerting to ensure pipeline reliability, performance, and cost efficiency.
• Enforce data governance best practices, including access control, privacy, documentation, and data lineage.

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