Role: Cloud data engineer
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, 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
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