Suvoda is seeking a skilled and driven Cloud Data Engineer to help evolve our data platform towards a data mesh architecture. In this role, you’ll design and build domain-oriented data products and support near real-time reporting.You’ll work on building and optimizing ETL/ELT pipelines using AWS Glue and PySpark, ensuring scalable, high-performance data processing across our platform.

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.

Requirements:

— Bachelor’s degree in a technical field such as Computer Science or Mathematics.

— At least 4 years of experience in data engineering, with demonstrated ownership of complex data systems.

— Solid experience with AWS data lake technologies (S3, Glue, Lake Formation, Athena, Redshift).

— Understanding of data mesh principles and decentralized data architecture.

— Proficiency in Python, SQL

— Experience with data modeling, orchestration tools (e.g., Airflow), and CI/CD pipelines.

— Strong communication and collaboration skills.

Preferred Qualifications:

— Master’s degree, especially with a focus on data engineering, distributed systems, or cloud architecture.

— Hands-on experience in infrastructure-as-code tools (e.g., Terraform, CloudFormation).

— Expertise in AWS Glue and PySpark for scalable ETL/ELT development.

— 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.