Description

Design, build, and maintain scalable data pipelines and analytics datasets that power enterprise reporting across Finance, Technology, and Operations.

Responsibilities

• Build and maintain Python-based ETL pipelines within AWS Glue, managing transformation logic, orchestration, and metadata cataloging.
• Architect and manage the enterprise data lake on Amazon S3 using Apache Iceberg, including partitioning strategies, schema evolution, and lifecycle management.
• Develop reporting-ready views and queries using Amazon Athena and AWS Glue metadata to support downstream analytics and AI tools.
• Implement and monitor data quality frameworks, including validation rules and anomaly detection, to ensure integrity of enterprise data assets.
• Collaborate with stakeholders to translate business requirements into scalable data architecture and establish data governance best practices.

Required Skills

• 7+ years of experience in data engineering, analytics engineering, or related roles.
• Strong proficiency in Python for building data transformation and ETL/ELT pipelines.
• Hands-on experience with AWS data platforms: S3, Athena, Glue, and AWS Unified Catalog.
• Experience integrating data from SaaS or enterprise systems using ETL tools such as SnapLogic.
• Strong SQL skills and experience building analytical datasets for reporting.
• Experience supporting BI tools such as Amazon QuickSight, Tableau, or Power BI.
• Bachelor’s degree in Computer Science, Engineering, Information Systems, or equivalent experience.

Key Skills

Education

Any Graduate