AWS Data engineer

Job Overview We are seeking an experienced Cloud Data Engineer to the architecture, development, Data migration, ETL pipeline in cloud and optimization of a next-generation data platform. This is a development role for an individual with over 4 years of deep data engineering and data migration, data pipeline in AWS expertise, capable of driving technical direction, and delivering high-impact solutions in a fast-paced

Key Responsibilities

• Platform Development
• Knowledge of best practices for our end-to-end data pipelines in Cloud, ensuring scalability, reliability, and security across the platform.
• Aggressively folowing the project timeline to ensure rapid delivery of data migratation in AWS RDS.
• Working with Data Analysts, and business stakeholders to translate complex requirements into robust, production-ready ETL data solutions.
• Data Pipeline Development
• Ingestion & Transformation: Design, build, and optimize high-volume data ingestion and transformation jobs using tools like dbt Core, iceberg, AWS Glue, ensuring data quality and integrity.
• Building ELT/ETL pipelines and using workflow orchestration tools like Apache Airflow or dbt
• Optimize large-scale data processing algorithms and cloud data warehouse performance.
• Workflow: Develop and maintain sophisticated data pipelines using AWS glue/lambda, focusing on modularity and reusability.
• Streaming & Real-time Integration: Implement and manage real-time data flows utilizing native AWS streaming services (e.g., Kinesis) for immediate data availability.
• Data Security and Privacy: Data Anonymization, Compliance with Regulations
• Good knowledge of CI/CD for ETL pipeline automation and Data migration
• Drive the Data Ecosystem Management & Monitoring
• Open Table Formats & Management: Implement and maintain the Iceberg open table format, utilizing tools for efficient schema evolution and data management.
• Compute Engine Optimization: Optimize query performance and cost efficiency across our primary compute engines: Amazon Redshift, and AWS Athena.
• rapidly identify bottlenecks, and troubleshoot production issues

Candidate Profile

• 4+ Years of hands-on, progressive experience in Cloud Data Engineering, cloud ecosystems AWS, Data Architecture
• Deep conceptual understanding of core data engineering principles, ETL/ELT patterns, and metadata management
•  Ability to translate complex business needs into actionable technical requirements.
•  Strong troubleshooting and problem-solving skills in distributed systems.
•  Agile/Scrum teamwork capabilities and version control practices
• Insurance industry experience preferred but not mandatory
• Tools:
• Cloud Environment: AWS (S3, IAM, VPC, Glue, Lambda etc.)
• Experience with dbt Core, Iceberg, AWS Glue Catalog, AWS lambda, Redshift, Athena, Splunk, AmazonQ, AWS streaming services, Git
• Strong SQL, Pyspark and Python Skills: pipeline,etl,aws,dbt,data,management,cloud