Senior Data Engineer AWS DevOps Big Data
2 days ago
Our client is seeking 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.
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.
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