Data Engineer

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).