Lead AWS Data Engineer

Role: Lead AWS Data Engineer Location: Houston, TX-Onsite Duration: Long Term Contract Minimum Requirements: • Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering, or a related discipline plus at least 12 years of hands-on data engineering experience, or demonstrated equivalency of experience and/or education • 5+ years in a technical-lead or team-lead capacity delivering enterprise-grade solutions. • Deep expertise in AWS data and analytics services: e.g.; S3, Glue, Redshift, Athena, EMR/Spark, Lambda, IAM, and Lake Formation. • Proficiency in Python/PySpark or Scala for data engineering, along with advanced SQL for warehousing and analytics workloads. • Demonstrated success designing and operating large-scale ELT/ETL pipelines, data lakes, and dimensional/columnar data warehouses. • Experience with workflow orchestration (e.g.; Airflow, Step Functions) and modern DevOps practices—CI/CD, automated testing, and infrastructure-as-code (e.g.; Terraform or CloudFormation). • Experience with data lakehouse architecture and frameworks (e.g.; Apache Iceberg). • Experience in integrating with enterprise (onprem, SaaS) systems (Oracle e-business, Salesforce, Workday) • Strong communication, stakeholder-management, and documentation skills; aptitude for translating business needs into technical roadmaps. Preferred Qualifications: • Solid understanding of data modeling, data governance, security best practices (encryption, key management), and compliance requirements. • Experience working within similarly large, complex organizations • Experience building integrations for enterprise back-office applications • AWS Certified Data Analytics – Specialty or AWS Solutions Architect certification (or equivalent) preferred; experience with other cloud platforms is a plus. • Proficiency in modern data storage formats and table management systems, with a strong understanding of Apache Iceberg for managing large-scale datasets and Parquet for efficient, columnar data storage. • In-depth knowledge of data cataloging, metadata management, and lineage tools (AWS Glue Data Catalog, Apache Atlas, Amundsen) to bolster data discovery and governance. • Knowledge of how machine learning models are developed, trained, and deployed, as well as the ability to design data pipelines that support these processes. • Experience migrating on-prem data sources onto AWS. • Experience building high quality Data Products.