Senior Data Engineer (AWS Hands-On)

Dice is the leading career destination for tech experts at every stage of their careers. Our client, PDDN Inc, is seeking the following. Apply via Dice today!

Job Title: Senior Data Engineer (AWS - Hands-On)

Location: Manhattan, NY (Hybrid - 2 days onsite/week)

Duration: 6 Months (High possibility of extension)

Employment Type: Contract

Billing: 35 hours/week - Only W2 Consultants

Role Overview

We are seeking a highly hands-on Senior Data Engineer to design, build, and maintain scalable data pipelines and analytics platforms within a cloud-native AWS environment.

This role requires deep technical expertise, strong ownership, and the ability to clear rigorous coding interviews. Candidates must demonstrate real-time problem-solving skills and hands-on implementation experience.

MUST HAVE (Strict Screening Criteria)

• 14+ years overall IT experience
• 7+ years of strong hands-on Data Engineering experience
• Expert-level SQL skills (complex transformations, performance tuning, analytical datasets)
• Strong hands-on experience with AWS stack:
• S3 (Data Lake)
• AWS Glue (ETL development using Python)
• Athena (querying & optimization)
• Lambda, Step Functions (orchestration)
• AWS Data Catalog
• Hands-on Python development (ETL pipelines - not just scripting)
• Proven experience building end-to-end data pipelines (ETL/ELT)
• Experience working with large-scale enterprise data systems
• Must be able to code live during interviews (no proxy / no support)

Highly Preferred

• Experience with Apache Iceberg (Data Lake architecture)
• Experience with SnapLogic or similar ETL tools
• Exposure to dbt and Snowflake
• Experience with BI tools:
• Amazon QuickSight
• Tableau / Power BI
• Experience in Banking / Payments / Financial Systems
• ISO 20022, SWIFT, NACHA (plus)
• Experience working in regulated environments

Key Responsibilities

• Design and develop scalable AWS-based data pipelines
• Build and manage enterprise data lake (S3 + Iceberg architecture)
• Develop ETL/ELT pipelines using Python & AWS Glue
• Create reporting-ready datasets for analytics & BI tools
• Implement data quality checks, validation, and monitoring frameworks
• Optimize query performance and data processing efficiency
• Collaborate with Finance, Technology, and Operations stakeholders
• Ensure data governance, lineage, security, and compliance
• Troubleshoot production issues and maintain pipeline reliability