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:
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Cloud DevOps & Big Data Administration:
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Manage and optimize big data environments on AWS, with a focus on efficient administration and maintenance.
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Leverage AWS Lake Formation to design and implement data lakehouses versus data fabric architectures, ensuring data integrity and accessibility.
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Data Engineering & Spark Management:
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Develop and maintain Spark jobs, with a focus on auditing, monitoring, and instrumentation to ensure reliability and performance.
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Perform Spark performance tuning, including understanding and applying Spark 3+ features, such as Adaptive Query Execution (AQE) and job-level resource management.
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Create services and tools to manage a multi-tenant environment, ensuring seamless data operations across tenants.
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Infrastructure as Code (IaC):
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Utilize Terraform for infrastructure provisioning and management, ensuring scalable and secure environments.
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Integrate with AWS Glue and HIVE for data processing and management, optimizing workflows for large-scale data operations.
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Data Storage & Management:
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Work with data storage formats like Parquet and Hudi to optimize data storage and retrieval.
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Implement and manage IAM policies for secure data access and management across AWS services.
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Collaboration & Continuous Improvement:
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Collaborate with cross-functional teams, including data scientists, analysts, and other engineers, to develop and deploy data solutions.
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Continuously improve data engineering practices, leveraging new tools and techniques to enhance performance and efficiency.
Required Skills:
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Cloud DevOps & Big Data:
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Extensive experience in cloud DevOps and big data administration on AWS.
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Proficiency in AWS Lake Formation, with a strong understanding of data lakehouse versus data fabric concepts.
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Programming & Data Engineering:
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Expertise in Python for data processing and automation.
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Deep knowledge of Spark internals (Spark 3+ features), including architecture, events, system metrics, AQE, and job-level resource management.
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Tech Stack:
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Strong hands-on experience with Terraform for infrastructure management.
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Experience with data formats such as Hudi and Parquet.
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Familiarity with AWS Glue, HIVE, and IAM for data management and security.
Good to Have:
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Additional Tools & Technologies:
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Knowledge of Iceberg and its application in data storage and management.
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Experience with Airflow for workflow automation.
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Familiarity with Terragrunt for managing Terraform configurations.
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Understanding of DynamoDB and its integration within data environments.