AWS Data Engineer with Big Data Processing
4 weeks ago
Data Engineering Role
A highly skilled AWS Data Engineer is required to design and implement robust data pipelines for big data processing. The ideal candidate will have expertise in AWS services such as EMR, S3, RDS, and Redshift, as well as experience with container orchestration using Kubernetes.
Key Responsibilities
- Data Pipeline Development: Design and implement ETL processes to extract, transform, and load data from various sources into data lakes and warehouses.
- AWS EMR Clusters: Configure, manage, and optimize Amazon EMR clusters for big data processing using Apache Spark, Hive, or Presto.
- Container Orchestration: Utilize Kubernetes for deploying, scaling, and managing containerized applications and services.
- CI/CD Implementation: Develop and maintain CI/CD pipelines for automated deployment of data applications and services using tools like Jenkins, GitLab CI, or AWS CodePipeline.
- SQL Development: Write complex SQL queries for data manipulation and retrieval, ensuring high performance and scalability.
- Data Quality and Governance: Implement data quality checks, monitoring, and logging mechanisms to ensure data reliability and compliance.
- Collaboration: Work closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions that meet business needs.
- Documentation: Maintain comprehensive documentation of data architecture, processes, and workflows.
Qualifications
- Education: Bachelor's degree in Computer Science, Data Science, Information Technology, or a related field.
- Experience: 3+ years of experience in data engineering or a related role, with a focus on AWS technologies.
- AWS Expertise: Proficient in AWS services such as EMR, S3, RDS, Redshift, Lambda, and CloudFormation.
- Kubernetes Knowledge: Experience with Kubernetes for container orchestration and microservices architecture.
- CI/CD Tools: Familiarity with CI/CD tools and practices, including version control using Git.
- SQL Proficiency: Strong knowledge of SQL, with experience in relational databases (e.g., MySQL, PostgreSQL) and data warehousing solutions.
- Programming Skills: Proficiency in programming languages such as Python, Java, or Scala for data processing tasks.
- Problem-Solving Skills: Ability to troubleshoot complex data issues and optimize performance.
- Communication: Excellent communication skills with the ability to work collaboratively in a team environment.
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