Senior Data Platform Engineer
| Dyddiad hysbysebu: | 01 Ebrill 2026 |
|---|---|
| Oriau: | Llawn Amser |
| Dyddiad cau: | 01 Mai 2026 |
| Lleoliad: | Manchester, Greater Manchester |
| Gweithio o bell: | Hybrid - gweithio o bell hyd at 5 ddiwrnod yr wythnos |
| Cwmni: | NCC Group |
| Math o swydd: | Parhaol |
| Cyfeirnod swydd: | R10104 |
Crynodeb
Senior Data Platform Engineer
Manchester, Cheltenham or London
We are seeking a skilled Data Engineer to join our Engineering team, responsible for designing, building, and optimising scalable data pipelines that power advanced analytics and machine learning solutions. You will play a key role in enabling data-driven decision-making by delivering high-quality, reliable datasets to tools such as Amazon SageMaker and other analytics platforms.
We are looking for a Data Engineer to work closely with the Data Science team to develop robust data pipelines that feed analytics and machine learning tools such as Amazon SageMaker and third-party platforms like Databricks. You will leverage AWS technologies such as EMR, S3, EKS and Airflow to process and orchestrate high-volume datasets, ensuring solutions are scalable, resilient and cost-efficient. You will also play a key role in embedding data loss prevention (DLP) principles and controls into data pipelines to protect sensitive information, while ensuring data is reliable, accessible, well-governed and optimised for downstream consumption.
What we are looking for in you:
Essential
• Strong experience in data engineering within AWS cloud environments.
• Hands-on experience with AWS big data technologies such as EMR, S3 and SageMaker.
• Proficiency in Python for building scalable data pipelines and processing frameworks. Experience with Apache Spark for distributed data processing.
• Experience designing and maintaining scalable batch and real-time data pipelines.
• Solid understanding of ETL/ELT design patterns and data modelling techniques.
• Experience with workflow orchestration tools such as Apache Airflow (ideally deployed on AWS).
• Familiarity with containerisation and orchestration using Docker and Kubernetes (EKS).
• Experience with infrastructure as code (e.g. Terraform) and CI/CD/GitOps practices.
• Proven ability to optimise performance and reduce cloud costs through partitioning, clustering and workload management.
• Understanding of data security principles, including data loss prevention (DLP).
Desirable
• Experience with Databricks or similar third-party big data platforms.
• Knowledge of real-time streaming technologies (e.g. Kafka, Kinesis).
• Experience implementing data governance and compliance frameworks.
• Familiarity with monitoring and observability tools in AWS environments.
• Exposure to Lakehouse or modern data platform architectures.
Manchester, Cheltenham or London
We are seeking a skilled Data Engineer to join our Engineering team, responsible for designing, building, and optimising scalable data pipelines that power advanced analytics and machine learning solutions. You will play a key role in enabling data-driven decision-making by delivering high-quality, reliable datasets to tools such as Amazon SageMaker and other analytics platforms.
We are looking for a Data Engineer to work closely with the Data Science team to develop robust data pipelines that feed analytics and machine learning tools such as Amazon SageMaker and third-party platforms like Databricks. You will leverage AWS technologies such as EMR, S3, EKS and Airflow to process and orchestrate high-volume datasets, ensuring solutions are scalable, resilient and cost-efficient. You will also play a key role in embedding data loss prevention (DLP) principles and controls into data pipelines to protect sensitive information, while ensuring data is reliable, accessible, well-governed and optimised for downstream consumption.
What we are looking for in you:
Essential
• Strong experience in data engineering within AWS cloud environments.
• Hands-on experience with AWS big data technologies such as EMR, S3 and SageMaker.
• Proficiency in Python for building scalable data pipelines and processing frameworks. Experience with Apache Spark for distributed data processing.
• Experience designing and maintaining scalable batch and real-time data pipelines.
• Solid understanding of ETL/ELT design patterns and data modelling techniques.
• Experience with workflow orchestration tools such as Apache Airflow (ideally deployed on AWS).
• Familiarity with containerisation and orchestration using Docker and Kubernetes (EKS).
• Experience with infrastructure as code (e.g. Terraform) and CI/CD/GitOps practices.
• Proven ability to optimise performance and reduce cloud costs through partitioning, clustering and workload management.
• Understanding of data security principles, including data loss prevention (DLP).
Desirable
• Experience with Databricks or similar third-party big data platforms.
• Knowledge of real-time streaming technologies (e.g. Kafka, Kinesis).
• Experience implementing data governance and compliance frameworks.
• Familiarity with monitoring and observability tools in AWS environments.
• Exposure to Lakehouse or modern data platform architectures.