Data Engineer

Job Summary

Responsibilities

Key Responsibilities:

• Design and develop datapipelines to collect, transform, and load data into datalakes and datawarehouses .
• Optimize ETLworkflows to ensure data accuracy, reliability, and scalability.
• Collaborate with data analysts, data scientists, and business stakeholders to understand data requirements.
• Implement and manage cloud − baseddataplatforms (e.g., AWS , Azure , or GoogleCloudPlatform ).
• Develop datamodels to support analytics and reporting.
• Monitor and troubleshoot data systems to ensure high performance and minimal downtime.
• Ensure data quality and security through governance best practices.
• Document workflows, processes, and architecture to facilitate collaboration and scalability.
• Stay updated with emerging data engineering technologies and trends.
  

Qualifications

Required Skills and Qualifications:

• Strong proficiency in SQL and Python for data processing and transformation.
• Hands-on experience with bigdatatechnologies like ApacheSpark , Hadoop , or Kafka .
• Knowledge of datawarehousingconcepts and tools such as Snowflake , BigQuery , or Redshift .
• Experience with workfloworchestrationtools like ApacheAirflow or Prefect .
• Familiarity with cloudplatforms (AWS, Azure, GCP) and their data services.
• Understanding of datagovernance , security , and compliance best practices.
• Strong analytical and problem-solving skills.
• Excellent communication and collaboration abilities.
  

Preferred Qualifications

• Certification in cloudplatforms (AWS, Azure, or GCP).
• Experience with NoSQLdatabases like MongoDB , Cassandra , or DynamoDB .
• Familiarity with DevOpspractices and tools like Docker , Kubernetes , and Terraform .
• Exposure to machinelearningpipelines and tools like MLflow or Kubeflow .
• Knowledge of datavisualizationtools like PowerBI , Tableau , or Looker .