Job Description

  • Data Infrastructure Design and Maintenance: Architect, maintain, and enhance analytical and operational services and infrastructure, including data lakes, databases, data pipelines, and metadata repositories, to ensure accurate and timely delivery of actionable insights.
  • Collaboration: Work closely with data science teams to design and implement data schemas and models, integrate new data sources with product teams, and collaborate with other data engineers to implement cutting-edge technologies in the data space.
  • Data Processing: Develop and optimize large-scale batch and real-time data processing systems to support the organization's growth and improvement initiatives.
  • Workflow Management: Utilize workflow scheduling and monitoring tools like Apache Airflow and AWS Batch to ensure efficient data processing and management.
  • Quality Assurance: Implement robust testing strategies to ensure the reliability and usability of data processing systems.
  • Continuous Improvement: Stay abreast of emerging technologies and best practices in data engineering, and propose and implement optimizations to enhance development efficiency.

Job Requirement

  • Technical Expertise: Proficient in Unix environments, distributed and cloud computing, Python frameworks (e.g., pandas, pyspark), version control systems (e.g., git), and workflow scheduling tools (e.g., Apache Airflow).
  • Database Proficiency: Experience with columnar and big data databases like Athena, Redshift, Vertica, and Hive/Hadoop.
  • Cloud Services: Familiarity with AWS or other cloud services like Glue, EMR, EC2, S3, Lambda, etc.
  • Containerization: Experience with container management and orchestration tools like Docker, ECS, and Kubernetes.
  • CI/CD: Knowledge of CI/CD tools such as Jenkins, CircleCI, or AWS CodePipeline.


Nice-to-have requirements:

  • Programming Languages: Familiarity with JVM languages like Java or Scala.
  • Database Technologies: Experience with RDBMS (e.g., MySQL, PostgreSQL) and NoSQL databases (e.g., DynamoDB, Redis).
  • BI Tools: Exposure to enterprise BI tools like Tableau, Looker, or PowerBI.
  • Data Science Environments: Understanding of data science environments like AWS Sagemaker or Databricks.
  • Monitoring and Logging: Knowledge of log ingestion and monitoring tools like ELK stack or Datadog.
  • Data Privacy and Security: Understanding of data privacy and security tools and concepts.
  • Messaging Systems: Familiarity with distributed messaging and event streaming systems like Kafka or RabbitMQ.

HR1TECH

  • Company size:
    100 - 499
  • Your address:
    Hồ Chí Minh
  • Website:
    https://hr1tech.com/

Chuyên cung cấp các giải pháp về tuyển dụng nhân sự.

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