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