Senior Python ETL Developer (AWS Glue)

Industry: Cloud Data Engineering and Managed Cloud Services — delivering scalable ETL and analytics solutions on AWS for enterprise clients across finance, retail, and digital-native businesses. Sector: Data platform engineering, ETL automation, and operational analytics in a remote-first service model.

Primary title: Senior Data Engineer (AWS Glue & Python)

Role & Responsibilities

• Design, build, and maintain scalable ETL pipelines using AWS Glue and PySpark to ingest, transform, and prepare data for analytics and ML workloads.
• Develop reusable Python-based Glue jobs, Glue Studio workflows, and Glue ETL scripts to process batch and near-real-time data into S3 and downstream data stores.
• Optimize job performance through partitioning, predicate pushdown, broadcast joins, and tuning of Spark configurations for cost and latency efficiency.
• Implement data quality checks, schema evolution handling, and automated testing to ensure reliable data delivery and auditability.
• Author and maintain infrastructure-as-code for data pipelines (CloudFormation/Terraform) and integrate CI/CD pipelines for Glue job deployment and versioning.
• Collaborate with data architects, analysts, and DevOps teams to onboard new sources, define data models, and instrument monitoring/alerting for pipeline health.

Skills & Qualifications Must-Have

• Python
• PySpark
• AWS Glue
• SQL
• AWS S3
• Git

Preferred

• Terraform
• Apache Airflow
• AWS Glue Data Catalog

Qualifications (summary): Proven hands-on experience building production ETL pipelines on AWS with Glue and Spark; strong SQL and data modeling capability; familiarity with infrastructure-as-code and CI/CD for data workloads. US-based candidates (remote). Strong debugging, observability, and cost-optimization mindset expected.

Benefits & Culture Highlights

• Remote-first, flexible work environment with global cross-functional teams.
• Competitive compensation, continuous learning budget, and cloud certification support.
• High-impact projects across large enterprise data platforms with clear career growth paths.