
Data Engineer
hace 3 semanas
Description
- Design, build, and optimize scalable ETL pipelines using Apache Spark on Amazon EMR.
- Work closely with data scientists, analysts, and other engineering teams to define, implement, and maintain high-performance data infrastructure.
- Develop and maintain automated data workflows and processes for efficient data ingestion, transformation, and loading.
- Implement best practices for data engineering, including monitoring, logging, and alerting for data pipelines.
- Collaborate with stakeholders to understand business requirements and translate them into technical solutions.
- Optimize performance of data processing jobs and troubleshoot issues with large-scale distributed systems.
- Drive innovation in data infrastructure, evaluating and integrating new tools, frameworks, and approaches.
**Requirements**:
**Qualifications**:
- 5+ years of experience in data engineering, with at least 3+ years working with Apache Spark and Amazon EMR.
- Strong programming skills in Python and Scala with a focus on performance tuning and optimization for Spark jobs.
- Deep understanding of distributed computing concepts, data partitioning, and resource management in large-scale data processing systems.
- Proficiency in building and maintaining ETL pipelines for structured and unstructured data.
- Hands-on experience with AWS services such as S3, Lambda, EMR, Glue, and RDS.
- Strong problem-solving skills and ability to debug complex systems. Preferred Qualifications:
- Experience with DevOps practices, including CI/CD, infrastructure as code (e.g., Terraform, CloudFormation), and containerization (e.g., Docker).
- Experience with Kubernetes and container orchestration for Spark jobs.
- Familiarity with streaming data processing using tools like Kafka, Kinesis, or Flink.
- Experience with modern data lake architectures, including Delta Lake or Iceberg.
- AWS Certification (e.g., AWS Certified Big Data - Specialty, AWS Certified Solutions Architect) is a plus
5 years
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