Senior Data Engineer

Summary

Join Suvoda as a Senior Data Engineer and contribute to the evolution of our data platform towards a data mesh architecture. Design and build domain-oriented data products using GraphQL APIs and support near real-time reporting. You will build and optimize ETL/ELT pipelines using AWS Glue and PySpark, ensuring scalable data processing. Responsibilities include implementing AWS DMS pipelines for data replication and supporting data governance. Collaborate with various teams to deliver robust data solutions and contribute to automation and CI/CD practices. Stay up-to-date with emerging technologies to enhance the platform.

Requirements

• Bachelor’s degree in a technical field such as Computer Science or Mathematics
• At least 5 years of experience in data engineering, with demonstrated ownership of complex data systems
• Experience working with GraphQL APIs for data access
• Strong expertise in AWS Glue and PySpark for scalable ETL/ELT development
• Solid experience with AWS data lake technologies (S3, Glue, Lake Formation, Athena, Redshift)
• Hands-on experience with AWS DMS and Aurora PostgreSQL for real-time data replication
• Understanding of data mesh principles and decentralized data architecture
• Proficiency in Python, SQL, and infrastructure-as-code tools (e.g., Terraform, CloudFormation)
• Experience with data modeling, orchestration tools (e.g., Airflow), and CI/CD pipelines
• Strong communication and collaboration skills

Responsibilities

• Contribute to the design and implementation of a data mesh architecture using GraphQL APIs to expose domain-owned data products
• Build and maintain a modern AWS-based data lake using S3, Glue, Lake Formation, Athena, and Redshift
• Develop and optimize ETL/ELT pipelines using AWS Glue and PySpark to support batch and streaming data workloads
• Implement AWS DMS pipelines to replicate data into Aurora PostgreSQL for near real-time analytics and reporting
• Support data governance, quality, observability, and API design best practices
• Collaborate with product, engineering, and analytics teams to deliver robust, reusable data solutions
• Contribute to automation and CI/CD practices for data infrastructure and pipelines
• Stay current with emerging technologies and industry trends to help evolve the platform

Preferred Qualifications

• Master’s degree, especially with a focus on data engineering, distributed systems, or cloud architecture
• Experience with event-driven architectures (e.g., Kafka, Kinesis)
• Familiarity with data cataloging and metadata management tools
• Knowledge of data privacy and compliance standards (e.g., GDPR, HIPAA)
• Background in agile development and DevOps practices