Cloud Data Engineer

Hi,

The following requirement is open with our client.

Title                                       : Cloud Data Engineer

Location                               : Seattle WA/ Dallas, TX/ Plano, TX - Onsite

Duration                              : 12+ Months

Rate                                        : $45-50/hr on W2

Relevant Experience (in Yrs.):

Job Description:

· 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, Google Cloud Platform)

· 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 integrationenvironment.

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

Thanks & regards,

Balu Garibe

ASCII Group, LLC
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