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Suvoda

Data Engineer

Remote Senior $4.7k–$12.8k/moest.

Summary

Suvoda is seeking a skilled Cloud Data Engineer to evolve their 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 for scalable, high-performance data processing.

What you'll do

  • 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

Required:

  • 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 and SQL
  • Experience with data modeling, orchestration tools (e.g., Airflow), and CI/CD pipelines
  • Strong communication and collaboration skills

Preferred:

  • 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

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