Cloud Data Engineer
Role : 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:
Roles & Responsibilities:
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
