Cloud Data Engineer

Cloud Data Engineer

Location: Seattle WA/ Dallas/ Plano (Seattle Preffered)

Duration: 6 months (can extend)

Pay Rate: $35 - $45/hr on W2/ C2C.

Must Have Skills:

• 5+ years of experience building platforms and data products
• 5+ years of experience working with data and data warehousing business solutions
• 5+ years of experience with cloud-platform technologies (AWS, Azure, GCP)
• AWS data engineering services (Glue, Lambda, Redshift), data modeling/Data mesh
• 5+ years of experience with data pipeline tools and Extract Transform Load (ETL) services
• Experience with the configuration of Application Programming Interfaces (APIs) for data ingestion
• Experience with Artificial Intelligence (AI) hardware and software integration environment.

Roles & Responsibilities:

• Design and own the end-to-end cloud data platform architecture for Human Resources (HR) data (ingest, storage, processing, serving, cataloging, and archival)
• Translate HR analytics and Machine Learning (ML) requirements into logical and physical data models, data products, and platform services
• Define and implement data ingestion strategies (batch, streaming), transformation patterns (ELT/ETL), and orchestration for HR sources (HRIS, payroll, Learning Management Systems, recruiting, time and attendance, benefits, performance systems)
• Design, develop, maintain and optimize internal company data architecture for complex databases/data warehouse required to operate the business
• Complete data modelling for acquisition and database implementation collaborating with different stakeholders
• Apply data extraction, transformation and loading techniques to connect large and complex data sets from a variety of sources
• Lead the creation of data collection frameworks for structured and unstructured data and solve complex data problems to generate features required by data scientists
• Lead activities to develop and maintain complex infrastructure systems (e.g., data warehouses, data lakes) including data access Application Programming Interfaces (APIs)
• Analyze and manage complex data
• Guide other data and analytics professionals on data standards and practices
• Create a culture of sharing, reuse, design for scale stability, and operational efficiency of data and analytics solutions
• Lead build of repeatable data pipelines across complex multi- and hybrid-cloud environments · Leverage automation extensively for scalability, repeatability, and reuse