Associate Performance Engineer

Responsibilities

• Assist with Performance Tuning: collaborate with a senior engineer to analyze and tune Spark/PySpark and AWS Glue job performance, including partitioning, clustering, batch sizing, and file format optimization such as Parquet, ORC, Iceberg.
• Support Query Optimization: optimize query performance across Athena, Trino, PostgreSQL, Redshift, and semantic-layer objects, contributing to efficient execution of user queries, reports, and dashboards.
• Collaborative Troubleshooting: identify, diagnose, and resolve failed production jobs and performance bottlenecks.
• Support Performance Testing: participate in periodic load and performance tests; define benchmarks, document and analyze results, and draft test reports with recommendations for system improvements.
• Pipeline Monitoring: monitor pipeline health using Cloud Watch, ETL metadata, log analysis, and dashboards; harden and optimize error handling and automated job notifications.
• Team
  
  Collaboration:
  
  coordinate with developers, testers, and stakeholders to resolve technical issues; participate actively in agile ceremonies and Program Increment planning events.
• Production Operations Support: work as part of a team to support production operations across a large data pipeline portfolio.

• Bachelor's degree in computer science, Information Systems, Engineering, or related technical discipline
• In lieu of a degree, four additional years of related, specialized experience is required
• Minimum of 4 years of experience in performance engineering, data engineering, or quality engineering for large‑scale, data‑centric systems
• Hands‑on experience tuning Apache Spark/PySpark workloads and AWS Glue jobs
• Experience with workflow orchestration using Airflow/MWAA or similar tools
• Experience optimizing queries and storage across S3‑based data lakes (Iceberg/Parquet), query engines (Athena or Trino), and relational databases (PostgreSQL, Redshift, or Oracle)
• Foundational proficiency in Python and SQL
• Experience with load/performance testing and production troubleshooting of automated data pipelines at scale
• Strong analytical, root‑cause analysis, and communication skills
• Ability to obtain and maintain a Moderate Risk Public Trust clearance; residing in the United States

Demonstrates expertise in performance tuning and optimization of data pipelines using Spark/PySpark and AWS Glue, along with strong analytical skills for troubleshooting and enhancing query performance across various data storage and processing technologies.