About this role
The candidate will build and evolve scalable data and ML platform capabilities using Python and cloud-native services. Key responsibilities include designing and developing data ingestion pipelines and transformation frameworks using DBT and Python. The role involves implementing infrastructure as code with Terraform to manage cloud resources and ensuring platform reliability through monitoring and governance. The engineer will collaborate with cross-functional teams, including data and ML engineers, to support the development and deployment of supporting a leading UK retail client. The technical environment includes AWS or Azure, Apache Airflow, and potentially Snowflake.
Skills & technologies
Nice to have
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About The Role
In this role, you will build and evolve scalable data and ML platform capabilities using Python and cloud-native services. You will work with modern data stacks to deliver reliable ingestion pipelines, transformation frameworks, and infrastructure that enable data-driven decisions. You will collaborate with cross-functional teams supporting a leading UK retail client serving millions of customers.
Responsibilities
In this role, you will build and evolve scalable data and ML platform capabilities using Python and cloud-native services. You will work with modern data stacks to deliver reliable ingestion pipelines, transformation frameworks, and infrastructure that enable data-driven decisions. You will collaborate with cross-functional teams supporting a leading UK retail client serving millions of customers.
Responsibilities
- Design and develop scalable data and platform capabilities using Python, DBT, and cloud-native services, building reliable data ingestion pipelines and transformation frameworks that support machine learning and analytics workloads
- Drive ownership of platform reliability and performance by implementing monitoring solutions, governance controls, and ML infrastructure components that ensure secure, stable, and standards-aligned data and ML operations
- Collaborate closely with data engineers, ML engineers, and platform teams to support development, deployment, and monitoring of data and machine learning solutions across shared environments
- Implement infrastructure as code using Terraform to provision, configure, and manage cloud resources that power scalable data platforms and ML infrastructure across multiple environments
- Configure and manage cloud-based infrastructure components, applying established architecture patterns and configuration standards to maintain consistent and reliable platform environments
- Develop and maintain DBT-based transformation frameworks and Python-powered data pipelines to process, transform, and prepare data for analytics and machine learning use cases
- Troubleshoot platform and pipeline issues alongside senior engineers, identifying root causes and implementing improvements that enhance system stability and operational efficiency
- Contribute to technical discussions and platform design decisions, helping refine infrastructure patterns, engineering standards, and best practices for data and machine learning platforms
- Strong proficiency with Python development, including asynchronous programming and frameworks such as FastAPI and aiohttp
- Practical experience with SQL for querying, transforming, and managing relational data
- Hands-on experience with ETL pipeline development and orchestration using Apache Airflow
- Practical experience with infrastructure as code using Terraform
- Working experience with cloud platforms such as AWS or Azure for building and deploying data-driven services
- Foundational understanding of machine learning concepts, including general knowledge of how ML models are developed and used in applications
- Familiarity with Snowflake cloud data platform for data warehousing and analytics (nice-to-have)
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