The Lead Data Platform & Cloud Engineer will own and deliver the end-to-end technical execution of a data migration and platform modernisation and AI enablement programme in AWS.
This role is best suited to a well-rounded cloud/platform engineer with strong DevOps and infrastructure expertise, who can operate across data migration, platform engineering, MLOps/AI enablement, and modern cloud-native development in AWS.
The focus is initially on DevOps, infrastructure, and platform engineering, enabling scalable, secure, and automated migration and ingestion of enterprise data. Building a foundation for enabling scalable machine learning and AI-driven use cases (MLOps) is the ultimate goal.
Key Responsibilities
- Own and evolve the AWS/CDP architecture for data migration, ingestion, feature engineering and downstream AI/ML consumption
- Define and enforce engineering standards (Terraform, CI/CD, pipeline design, naming conventions)
- Define and implement MLOps architecture and standards, including model training, deployment and monitoring workflows (e.g. SageMaker pipelines())
- Lead platform engineering and infrastructure delivery (IaC, networking, security, environment setup)
- Lead and deliver the migration of ~8–10 enterprise databases into AWS using DMS and CDC patterns
- Ensure migration pipelines are scalable, automated, and resilient
- Enable event-driven and batch data and ML pipelines (S3, Lambda, orchestration), including pipelines that support MLOps.
- Enable AI and advanced analytics by ensuring data is discoverable, high quality and structured for ML consumption
- Ensure data lineage, cataloguing, and documentation are captured
- Coordinate backlog prioritisation, sprint planning, and delivery sequencing
- Validate deliverables for quality, performance, security, and operational readiness
- Act as senior technical interface with stakeholders and delivery partners
- Lead knowledge transfer and upskilling of engineers
Required Experience & Skills
- 5+ years in cloud, platform or data engineering roles, including leadership experience
- Proven experience with Infrastructure-as-Code (Terraform) and CI/CD pipelines
- Strong Python and SQL skills
- Understanding of data platforms (e.g. Snowflake/CDP) and ingestion pipelines
- Hands-on experience with AI/ML and MLOps frameworks (.e.g AWS SageMaker) and supporting infrastructure for model training, deployment
- Experience designing or supporting MLOps practices, including CI/CD for ML, model versioning, monitoring.
- Understanding of data requirements for ML, including feature engineering, training data pipelines and data quality considerations.
- Familiarity with modern development and deployment frameworks
- Strong stakeholder communication and technical leadership skills
- Experience in the Energy Sector (nice to have)
- Experience with dbt / ELT patterns (nice to have)
- Experience with C# (nice to have)
Ideal Profile
- Broad technologist with a DevOps / platform engineering or cloud architecture background, with exposure to AI/ML or data science workflows.
- Comfortable working across infrastructure, data platforms, and application layers
- Able to move between hands‑on delivery and technical leadership
The Lead Data Platform & Cloud Engineer will own and deliver the end-to-end technical execution of a data migration and platform modernisation and AI enablement programme in AWS.
This role is best suited to a well-rounded cloud/platform engineer with strong DevOps and infrastructure expertise, who can operate across data migration, platform engineering, MLOps/AI enablement, and modern cloud-native development in AWS.
The focus is initially on DevOps, infrastructure, and platform engineering, enabling scalable, secure, and automated migration and ingestion of enterprise data. Building a foundation for enabling scalable machine learning and AI-driven use cases (MLOps) is the ultimate goal.
Key Responsibilities
- Own and evolve the AWS/CDP architecture for data migration, ingestion, feature engineering and downstream AI/ML consumption
- Define and enforce engineering standards (Terraform, CI/CD, pipeline design, naming conventions)
- Define and implement MLOps architecture and standards, including model training, deployment and monitoring workflows (e.g. SageMaker pipelines())
- Lead platform engineering and infrastructure delivery (IaC, networking, security, environment setup)
- Lead and deliver the migration of ~8–10 enterprise databases into AWS using DMS and CDC patterns
- Ensure migration pipelines are scalable, automated, and resilient
- Enable event-driven and batch data and ML pipelines (S3, Lambda, orchestration), including pipelines that support MLOps.
- Enable AI and advanced analytics by ensuring data is discoverable, high quality and structured for ML consumption
- Ensure data lineage, cataloguing, and documentation are captured
- Coordinate backlog prioritisation, sprint planning, and delivery sequencing
- Validate deliverables for quality, performance, security, and operational readiness
- Act as senior technical interface with stakeholders and delivery partners
- Lead knowledge transfer and upskilling of engineers
Required Experience & Skills
- 5+ years in cloud, platform or data engineering roles, including leadership experience
- Proven experience with Infrastructure-as-Code (Terraform) and CI/CD pipelines
- Strong Python and SQL skills
- Understanding of data platforms (e.g. Snowflake/CDP) and ingestion pipelines
- Hands-on experience with AI/ML and MLOps frameworks (.e.g AWS SageMaker) and supporting infrastructure for model training, deployment
- Experience designing or supporting MLOps practices, including CI/CD for ML, model versioning, monitoring.
- Understanding of data requirements for ML, including feature engineering, training data pipelines and data quality considerations.
- Familiarity with modern development and deployment frameworks
- Strong stakeholder communication and technical leadership skills
- Experience in the Energy Sector (nice to have)
- Experience with dbt / ELT patterns (nice to have)
- Experience with C# (nice to have)
Ideal Profile
- Broad technologist with a DevOps / platform engineering or cloud architecture background, with exposure to AI/ML or data science workflows.
- Comfortable working across infrastructure, data platforms, and application layers
- Able to move between hands‑on delivery and technical leadership
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