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Lead AWS Data Engineer

United Kingdom, London · Job Posted June 15, 2026
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Job Description

We are seeking an accomplished and detail-oriented Lead Data Engineer – AWS to join our Data & AI practice. The successful candidate will bring deep expertise in data engineering, distributed data processing, and cloud-native platforms, with a strong focus on AWS-based data ecosystems. This role is critical in designing, building, and optimising end-to-end data pipelines and platforms, enabling scalable data processing, advanced analytics, and AI-driven solutions. You will play a key role in ensuring data quality, integrity, performance, and reliability, supported by strong engineering and testing practices. As a senior practitioner, you will collaborate with architects, engineers, and analysts to deliver secure, scalable, and high-performing data solutions, leveraging technologies such as AWS Glue, Python/PySpark, SQL, and configuration-driven frameworks (e.g., YAML). You will thrive in a collaborative, client-facing environment, with a passion for solving complex technical challenges, ensuring delivery excellence, and driving modernisation through cloud-native engineering practices.

Job Responsibility

  • Act as a senior engineer within data engineering and cloud platform initiatives, supporting delivery across complex transformation programmes
  • Collaborate with architects and stakeholders to define and implement scalable AWS-based data solutions
  • Contribute to solution design, estimation, and delivery planning
  • Lead engineering workstreams and ensure high-quality technical delivery
  • Design, build, and optimise scalable data pipelines and data processing frameworks on AWS
  • Develop and maintain ETL/ELT pipelines using: AWS Glue
  • Python / PySpark
  • SQL
  • Configuration-driven frameworks (e.g., YAML)
  • Implement robust data ingestion, transformation, and processing patterns
  • Build reusable data services, components, and frameworks
  • Define and implement testing strategies for data pipelines, ensuring reliability and accuracy
  • Validate data processing workflows using: Python / PySpark transformations
  • SQL-based validation logic
  • Configuration-driven orchestration
  • Develop automated testing, monitoring, and alerting solutions
  • Ensure: Data completeness
  • Data accuracy
  • Consistent transformation behaviour
  • Drive improvements in observability and pipeline resilience
  • Lead development on AWS services including: AWS Glue
  • S3-based data lakes
  • Supporting services within the AWS data ecosystem
  • Support implementation of modern data architectures, including data lakes and Lakehouse-style platforms
  • Optimise pipelines and jobs for performance, scalability, and cost efficiency
  • Define and implement data transformation logic aligned to business requirements
  • Support data modelling approaches for analytics and platform use cases
  • Ensure consistency, usability, and quality across data assets and pipelines
  • Collaborate with: Solution Architects
  • Data Engineers
  • Analysts and ML engineers
  • Provide technical leadership and mentoring to engineers within the team
  • Promote engineering best practices, automation, and reusable solutions
  • Contribute to engineering standards, documentation, and knowledge sharing
  • Ensure data quality, integrity, and reliability across data platforms
  • Implement and enforce secure coding and data handling practices
  • Support compliance with: GDPR
  • Regulated environment standards (where applicable)
  • Contribute to monitoring, auditing, and operational processes

Requirements

  • Proven experience in data engineering and cloud-based platform delivery
  • Strong understanding of distributed data processing and scalable system design
  • Ability to lead delivery while remaining hands-on technically
  • Strong analytical, problem-solving, and communication skills
  • Experience working in client-facing and delivery-focused environments
  • Ability to mentor and develop engineering teams
  • Strong hands-on experience with: AWS cloud services, especially AWS Glue
  • Python / PySpark for large-scale data processing
  • SQL for querying, transformation, and validation
  • Configuration-driven development (e.g., YAML)
  • Experience building and operating: Data pipelines
  • ETL/ELT workflows
  • Cloud-native data platforms
  • Familiarity with: Data lakes and Lakehouse concepts
  • Distributed processing frameworks (e.g., Apache Spark)
  • Strong understanding of: ETL vs ELT patterns
  • Performance tuning and optimisation
  • Experience with: Version control (Git)
  • CI/CD and DevOps practices

What we offer

  • Range of tailored benefits that support your physical, emotional, and financial wellbeing
  • Continuous growth and development opportunities
  • Opportunity to have flexible work options

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