W2 only | Cloud Data Engineer (AWS, HRIS) | Onsite - Seattle, WA / Dallas, TX / Plano, TX (Seattle highly preferred)

W2 only

Role: Cloud Data Engineer
Location: Seattle, WA / Dallas, TX / Plano, TX (Seattle highly preferred)
Duration: 6 months (possible extension)
Rate: $45/hr to $50/hr. on W2 
Work Type: 100% On-site 
Work Authorization: Must be authorized to work in the U.S. without sponsorship

Must Have Skills:

• 5+ years of experience building platforms and data products
• 5+ years of experience working with data and data warehousing solutions
• 5+ years of experience with cloud platforms (AWS, Azure, or Google Cloud Platform)
• Strong experience with AWS data engineering services (Glue, Lambda, Redshift)
• Experience with data modeling and Data Mesh concepts
• 5+ years of experience with data pipeline tools and ETL processes
• Experience configuring APIs for data ingestion
• Experience with AI hardware and software integration environments

Roles & Responsibilities:

• Design and own end-to-end cloud data platform architecture for HR data, including ingestion, storage, processing, serving, cataloging, and archival
• Translate HR analytics and ML requirements into data models, data products, and platform services
• Define and implement data ingestion strategies (batch and streaming), transformation patterns (ETL/ELT), and orchestration
• Work with HR systems such as HRIS, payroll, LMS, recruiting, time and attendance, benefits, and performance systems
• Design, develop, maintain, and optimize data architecture for complex databases and data warehouses
• Perform data modeling for acquisition and database implementation in collaboration with stakeholders
• Apply ETL techniques to integrate large and complex datasets from multiple sources
• Develop data collection frameworks for structured and unstructured data
• Solve complex data challenges to support feature engineering for data science use cases
• Build and maintain data infrastructure systems, including data lakes and warehouses
• Develop and manage data access APIs
• Analyze and manage complex datasets
• Provide guidance on data standards and best practices to data and analytics teams
• Promote a culture of data sharing, reuse, scalability, and operational efficiency
• Build repeatable data pipelines across multi-cloud and hybrid environments
• Leverage automation for scalability, repeatability, and efficiency