Cloud Data Engineer

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.

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