Our requirements
You are a good candidate if you have:
Qualifications
* Minimum 10 years of experience across Data Engineering, Data Platform Engineering, Analytics Engineering, BI Engineering, Data Science Enablement, or similar data and analytics roles
* At least 3 years of technical leadership experience in a complex data, analytics, BI, AI/ML, or platform delivery environment
* Proven experience leading, line-managing, or formally coordinating mixed technical teams, including performance management, coaching, capability planning, and work allocation
* Experience defining standards, reusable methods, architecture patterns, governance practices, and production-readiness expectations across multiple teams, sites, markets, or business units
* Experience managing delivery across multiple stakeholders, squads, technical disciplines, and enterprise platforms
Experience / Skills:
* Working across multiple environments such as development, test, staging, and production
* Designing pipelines that support multiple consumers and use cases
* Understanding how to build scalable and reusable data processing patterns
* Understanding how curated datasets are consumed by BI and analytics teams
* Supporting BI Developers in building reliable reporting and semantic-layer outputs
* Supporting the creation of trusted datasets for BI, dashboarding, and advanced analytics
* Helping reduce duplication across reports, datasets, and squad-specific logic
* Ensuring that reporting and analytics requirements are considered when designing upstream data pipelines
* Supporting alignment between operational reporting, historical reporting, and advanced analytics use cases
* Unit and integration testing for data pipeline, with data lineage and traceability
* Pipeline observability and operational monitoring
* Reconciliation between source and target datasets
* Handling pipeline failures, retries, and support processes
* Ensuring transformation logic and data dependencies are documented
* Supporting production readiness and handover of data products
* Ensuring reporting and analytical outputs are based on trusted and well-understood data
Optional
- AWS Step Functions
- EventBridge or event-driven data processing
- Lambda-based orchestration or lightweight integration patterns
- Iceberg, Delta, or similar table formats
- CI/CD deployment of AWS data engineering workloads
- Terraform-based AWS infrastructure deployment
- Understanding of Azure data platform concepts
- Working with Azure Data Lake, Azure Data Factory, Synapse, Fabric, or similar services
- Preparing data for semantic-layer or reporting consumption
- Supporting BI and analytics teams with curated, trusted, and model-ready datasets
- Understanding how data needs to be structured for Power BI or advanced analytics use cases
- Helping align AWS data preparation with Azure-based consumption patterns
- Understanding the hand-off between data engineering outputs and semantic-layer modelling
- Power BI exposure
- Semantic model / semantic layer experience
- Experience supporting advanced analytics teams
- Experience with operational dashboards or Grafana
- Experience with KPI and metric standardisation across multiple sites or OpCos.