Cloud Data Engineer

Veriipro; Seattle (WA); On-site; USD 120,000 - 150,000; Full time

Job summary

Veriipro in Seattle is seeking a skilled cloud data engineer to design, optimize, and maintain data architecture for HR data. Candidates should have over 5 years of experience in cloud data engineering and expertise with AWS services like Glue and Redshift. This role involves translating HR analytics requirements into effective data models and leading data integration efforts.

The ideal candidate will possess strong knowledge of ETL/ELT processes and will be adept at working with both structured and unstructured data in multi-cloud environments.

Qualifications

• 5+ years of experience in cloud data engineering and scalable data products.
• Hands-on experience with cloud platforms like AWS, Azure, or GCP.
• Expertise in AWS data engineering services and cloud data modeling.

Responsibilities

• Design and optimize cloud data platform architecture.
• Translate HR analytics into data models and platform services.
• Lead data modeling and implementation in collaboration with stakeholders.

Skills

Cloud data engineering

Data warehousing

AWS

Data modeling

ETL/ELT pipelines

API integration

Tools

AWS Glue

AWS Lambda

AWS Redshift

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.