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Designation Data Engineer

Job Description

Develop and maintain scalable data pipelines using tools like Apache Kafka, Apache Spark, or AWS Glue to ingest, process, and transform large datasets from various sources, ensuring productive data flow and processing.
Design and implement data models and schemas in data warehouses (e.g., Amazon Redshift, Google BigQuery) and data lakes (e.g., AWS S3, Azure Data Lake) to support analytics and reporting needs.
Collaborate with data scientists and analysts to understand data requirements, ensuring data availability and accessibility for analytics, machine learning, and reporting.
Utilize ETL tools and frameworks (e.g., Apache NiFi, Talend, or custom Python scripts)



to automate data extraction, transformation, and loading processes, ensuring data quality and integrity.
Monitor and optimize data pipeline performance using tools like Apache Airflow or AWS Step Functions, implementing best practices for data processing and workflow management.
Write, test, and maintain scripts in Python, SQL, or Bash for data processing, automation tasks, and data validation, ensuring high code quality and performance.
Implement CI/CD practices for data engineering workflows using tools like Jenkins, GitLab CI, or Azure DevOps, automating the deployment of data pipelines and infrastructure changes.
Collaborate with DevOps teams to integrate data solutions into existing infrastructure, leveraging Infrastructure as Code (IaC) tools like Terraform or AWS CloudFormation for provisioning and managing resources.
Manage containerized data applications using Docker and orchestrate them with Kubernetes, e

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