Data Platform Engineer

Amazon.com, Inc.
New York, NY, United States
3 months ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Experienced
Experience required
2 years minimum
Compensation
$80,000.0 - $200,000.0
Working hours
Shift work
Job source

Tech stack

Airflow Amazon Web Services Amazon Elastic Compute Cloud Amazon S3 Apache HTTP Server Unit Testing Code Review Databases Data as a Services Data Infrastructure Data Warehousing Software Debugging
+23 more
Digital Assets Amazon DynamoDB Apache Hive Identity and Access Management Python (Programming Language) PostgreSQL Object-Oriented Software Development Performance Tuning Query Optimization SQL Databases Datadog AWS Cdk S3 Bucket Snowflake Git Data Lakes Pyspark Git Flow AWS Glue Terraform Data Pipelines Jenkins Databricks

Job description

The Data Platform Engineer / DBA manages our database and data platform fleet, maintaining secure, optimized, and highly available databases, data warehouses, and lakehouses. Our stack is evolving: Redshift remains a core platform, and we are actively expanding into Databricks and open table format (Apache Iceberg on S3). The Data Platform Engineer / DBA works closely with engineering and analytics teams to design, implement, and maintain these systems., Operate Redshift clusters, Databricks workspaces, RDS/Aurora PostgreSQL instances, and supporting AWS infrastructure Perform user/security tasks across platforms: Redshift user/group management, Databricks Unity Catalog access controls, IAM Roles/Policies, RDS parameter and access management Design and maintain open table format data lakes using Apache Iceberg on S3, including compaction, snapshot management, partition strategies, and schema evolution Profile production workloads and develop strategies to run optimized clusters and workspaces with scale and efficiency Provide architecture guidance and support to technical leads; help develop code and SQL for data assets, identify performance tuning opportunities, and work with developers to improve production systems Contribute to and maintain automation scripts and internal tooling with production-quality code standards (unit tests, peer review, documentation, Git-based workflows) Develop monitoring and recovery automation to identify and resolve issues to meet our high SLA Research new technologies and develop proofs of concept; propose technical improvements and new capabilities Must Have Skills / Requirements

Requirements

  1. Experience with AWS data services: Redshift, RDS/Aurora PostgreSQL, S3, EC2, Athena, Glue, Lake Formation
  2. 3+ years
  3. Experience with Databricks: workspace administration, Unity Catalog, cluster management, Spark SQL, Delta Live Tables or similar
  4. 2+ years
  5. Experience with PostgreSQL or Aurora PostgreSQL: query tuning, replication concepts, pg_stat views, parameter groups
  6. 2+ years
  7. Experience with AWS Security: IAM (Users, Groups, Roles, Policies, Instance Profiles), Redshift/RDS security, S3 bucket policies
  8. 2+ years Nice to Have Skills / Preferred Requirements

  9. PySpark for data pipeline development
  10. AWS Glue with Iceberg or Delta Lake integration
  11. Snowflake administration
  12. Terraform or AWS CDK for infrastructure as code
  13. DynamoDB experience
  14. Feature Store Soft Skills:

  15. Ability to read, understand, debug, and extend complex Python codebases; comfort navigating large repositories with multiple modules and abstractions
  16. Strong analytical and critical thinking skills
  17. Ability to lead projects, prioritize, and multitask
  18. Deadline and detail-oriented
  19. Part of on-call rotation Technology Requirements:

  20. Experience with Apache Iceberg on S3: partition management, compaction, snapshot management, schema evolution
  21. Proficiency in Python for automation and shared tooling: object-oriented design, writing maintainable and testable code, participating in code review, and contributing to a shared Git-based codebase
  22. Experience automating database and platform operations using Python, Jenkins, Airflow, Datadog and Git Education / Certifications

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