AWS Data Engineer
1 day ago
AWS Data Engineer - GCH
must have:
- Proficiency with Matillion ETL: Using the Matillion ETL platform for data integration.
- Cloud Data Warehouses: Familiarity with cloud data warehouses like Snowflake, AWS Redshift, or Google BigQuery.
Key Responsibilities:
- Design & Develop Data Pipelines: Build and optimize scalable, reliable, and automated ETL/ELT pipelines using AWS services (e.g., AWS Glue, AWS Lambda, Redshift, S3) and Databricks.
- Cloud Data Architecture: Design, implement, and support in maintaining data infrastructure in AWS, ensuring high availability, security, and scalability. Work with lake houses, data lakes, data warehouses, and distributed computing.
- DBT Core Implementation: Lead the implementation of DBT Core to automate data transformations, develop reusable models, and maintain efficient ELT processes.
- Data Modelling: Build efficient data models to support required analytics/reporting.
- Optimize Data Workflows: Monitor, troubleshoot, and optimize data pipelines for performance and cost-efficiency in cloud environments. Utilize Databricks for processing large-scale data sets and streamlining data workflows.
- Data Quality & Monitoring: Ensure high-quality data by implementing data validation and monitoring systems. Troubleshoot data issues and create solutions to ensure data reliability.
- Automation & CI/CD: Implement CI/CD practices for data pipeline deployment and maintain automation for monitoring and scaling data infrastructure in AWS and Databricks.
- Documentation & Best Practices: Maintain comprehensive documentation for data pipelines, architectures, and best practices in AWS, Databricks, and DBT Core. Ensure knowledge sharing across teams.
Skills & Qualifications:
Required:
- Bachelor's / master's degree in computer science, Engineering or a related field.
- 4+ years of experience as a Data Engineer or in a similar role.
- Extensive hands-on experience with AWS services (S3, Redshift, Glue, Lambda, Kinesis, etc.) for building scalable and reliable data solutions.
- Advanced expertise in Databricks, including the creation and optimization of data pipelines, notebooks, and integration with other AWS services.
- Strong experience with DBT Core for data transformation and modelling, including writing, testing, and maintaining DBT models.
- Proficiency in SQL and experience with designing and optimizing complex queries for large datasets.
- Strong programming skills in Python/PySpark, with the ability to develop custom data processing logic and automate tasks.
- Experience with Data Warehousing and knowledge of concepts related to OLAP and OLTP systems.
- Expertise in building and managing ETL/ELT pipelines, automating data workflows, and performing data validation.
- Familiarity with CI/CD concepts, version control (e.g., Git), and deployment automation.
- Having worked under Agile project environment
Preferred:
- Experience with Apache Spark and distributed data processing in Databricks.
- Familiarity with streaming data solutions (e.g., AWS Kinesis, Apache Kafka).
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