Design & Development: Lead the design and development of custom AWS utilities that allow analysts to query disparate data sources (S3 and Redshift) with minimal effort and high productivity.
Pipeline Engineering: Build and maintain scalable data pipelines using AWS Glue, PySpark, and Spark SQL to ingest and transform data across platforms.
Resiliency & Scalability: Apply an "enterprise-grade" development mindset. Build solutions that are resilient, scalable, and capable of quick automated recovery in the event of failure.
Utility Creation: Create tools to streamline the user experience for analysts, such as building automated processes to convert SQL outputs for multi-source tables.
Infrastructure Ops: Utilize CloudFormation to manage and deploy infrastructure components.
Required Technical Skills
AWS Core: Deep proficiency in AWS Glue, S3, Lambda, IAM, and Step Functions.
Data Engineering: Expert-level knowledge of PySpark and Spark SQL.
Querying & SQL: Extensive experience with Athena and Presto; strong SQL skills for complex data manipulation.
Infrastructure as Code: Proven experience with CloudFormation for operational excellence.
Development Mindset: Ability to write clean, maintainable code for automation and custom utility development.
Dremio: Experience with Dremio implementation (reflections, views, using system tables).
Required Qualifications
6-8+ years of Data Engineering or IT experience.
4+ years of AWS cloud development experience.
Expert Python programming skills.
Advanced SQL development experience.
Strong hands-on experience with Apache Spark / PySpark.
Experience building cloud-native ETL pipelines.
Strong understanding of distributed computing.
Experience designing scalable data models.
Experience with Git and CI/CD pipelines.
Strong analytical and problem-solving skills.
Build scalable and auto recoverable pipelines.
Bachelor's degree in Computer Science, Engineering, Information Systems, or related discipline