Cloud Data Engineer
Listed on 2026-07-04
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IT/Tech
Data Engineering, Data Warehousing, Cloud Computing: Infrastructure & Operations
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.
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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