Cloud Data Engineer

Job Description*Roles and Responsibilities*

• Design, own, and optimize cloud data platform architecture for HR data, including ingestion, storage, processing, serving, cataloging, and archival.
• Translate HR analytics and ML requirements into logical and physical data models, data products, and platform services.
• Define and implement data ingestion strategies (batch, streaming), transformation patterns (ETL/ELT), and orchestration for HR sources such as HRIS, payroll, LMS, recruiting, time and attendance, benefits, and performance systems.
• Design, develop, and maintain data warehouses, data lakes, and internal company data architecture for complex databases.
• Lead data modeling for acquisition, database implementation, and platform integration in collaboration with business and technical stakeholders.
• Apply advanced ETL/ELT techniques to connect, transform, and integrate large, complex datasets from multiple sources.
• Develop frameworks to collect, process, and manage both structured and unstructured data, enabling feature generation for data scientists.
• Lead the creation and management of data access APIs and automation pipelines to ensure efficiency, scalability, and repeatability.
• Provide guidance to data and analytics teams on data standards, governance, and best practices.
• Foster a culture of sharing, reuse, scalability, stability, and operational efficiency in data and analytics solutions.
• Build and maintain repeatable, automated data pipelines across multi-cloud and hybrid-cloud environments.

*Must-Have Skills & Expertise*

• 5+ years of experience in cloud data engineering, data warehousing, and building scalable data products.
• Hands-on experience with cloud platforms such as AWS, Azure, or GCP.
• Expertise with AWS data engineering services (Glue, Lambda, Redshift) and cloud data modeling/Data Mesh.
• Strong experience in ETL/ELT pipelines and orchestration tools.
• Experience configuring APIs for data ingestion and integration.
• Knowledge of AI hardware/software integration and ML data requirements.
• Strong understanding of database design, data modeling, and complex data architectures.
• Familiarity with structured and unstructured data management, data lakes, and analytics frameworks.
• Experience leveraging automation for scalability, repeatability, and operational efficiency.