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: dbt,glue,management,etl,aws,cloud
