Senior Data Engineer AWS,DevOps,Big Data – Finance – London

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Data Engineer (AWS,DevOps,Big Data) – Finance – London

Day rate: £550 – £650 inside IR35

Duration: 6 months

Start: ASAP

Hybrid 2 – 3 days in office

My new client is looking for a highly skilled Data Engineer with expertise in cloud DevOps, big data administration, and data engineering on AWS. The ideal candidate will have a deep understanding of AWS Lake Formation, data product concepts, and Spark job management, including auditing, monitoring, and performance tuning. This role involves creating and managing services and tools to support a multi-tenant environment, ensuring optimal performance and scalability.

Key Responsibilities:

• Cloud DevOps & Big Data Administration:
  • Manage and optimize big data environments on AWS, with a focus on efficient administration and maintenance.
  • Leverage AWS Lake Formation to design and implement data lakehouses versus data fabric architectures, ensuring data integrity and accessibility.
• Data Engineering & Spark Management:
  • Develop and maintain Spark jobs, with a focus on auditing, monitoring, and instrumentation to ensure reliability and performance.
  • Perform Spark performance tuning, including understanding and applying Spark 3+ features, such as Adaptive Query Execution (AQE) and job-level resource management.
  • Create services and tools to manage a multi-tenant environment, ensuring seamless data operations across tenants.
• Infrastructure as Code (IaC):
  • Utilize Terraform for infrastructure provisioning and management, ensuring scalable and secure environments.
  • Integrate with AWS Glue and HIVE for data processing and management, optimizing workflows for large-scale data operations.
• Data Storage & Management:
  • Work with data storage formats like Parquet and Hudi to optimize data storage and retrieval.
  • Implement and manage IAM policies for secure data access and management across AWS services.
• Collaboration & Continuous Improvement:
  • Collaborate with cross-functional teams, including data scientists, analysts, and other engineers, to develop and deploy data solutions.
  • Continuously improve data engineering practices, leveraging new tools and techniques to enhance performance and efficiency.

Required Skills:

• Cloud DevOps & Big Data:
  • Extensive experience in cloud DevOps and big data administration on AWS.
  • Proficiency in AWS Lake Formation, with a strong understanding of data lakehouse versus data fabric concepts.
• Programming & Data Engineering:
  • Expertise in Python for data processing and automation.
  • Deep knowledge of Spark internals (Spark 3+ features), including architecture, events, system metrics, AQE, and job-level resource management.
• Tech Stack:
  • Strong hands-on experience with Terraform for infrastructure management.
  • Experience with data formats such as Hudi and Parquet.
  • Familiarity with AWS Glue, HIVE, and IAM for data management and security.

Good to Have:

• Additional Tools & Technologies:
  • Knowledge of Iceberg and its application in data storage and management.
  • Experience with Airflow for workflow automation.
  • Familiarity with Terragrunt for managing Terraform configurations.
  • Understanding of DynamoDB and its integration within data environments.