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# Data Engineer

- **Company:** Robert Half
- **Location:** Glendale, AZ, United States
- **Experience:** Experienced
- **Contract:** Temporary contract
- **Skills:** Amazon Web Services, Data Analysis, Microsoft Azure, Big Data, Continuous Integration, Data Architecture, Information Engineering, Extract Transform Load (ETL), DevOps, Dimensional Modeling, Python (Programming Language), Standard Sql, Unstructured Data, Data Processing, Enterprise Software Applications, Azure Data Factory, Snowflake, Git, AWS Glue, Data Management, Api Design, Data Pipelines, Databricks
- **Published:** October 8, 2026
- **Apply:** https://dejobs.org/x/x/C71C41A2554A434590562C0DCF04856A/job/

## About the Role

* 4+ years of experience in Data Engineering or a similar role.
 * Strong SQL and Python skills.
 * Experience with technologies such as Databricks, Snowflake, Azure Data Factory, AWS Glue, or similar tools.
 * Experience developing ETL/ELT pipelines.
 * Understanding of dimensional modeling and modern data architecture.
 * Experience working with APIs and enterprise application integrations.
 * Familiarity with Git, CI/CD, and DevOps practices.

Experience supporting large-scale data modernization or cloud migration initiatives is highly preferred.

## Description

Robert Half is working with a client who is looking to hire a Data Engineer to support a growing enterprise data and analytics initiative. This individual will help build and modernize the data pipelines, integrations, and cloud-based data infrastructure used across the organization.

The ideal candidate has experience working with large datasets, modern cloud data platforms, and production-grade ETL/ELT pipelines. This role will work closely with Data Architects, Analytics Engineers, BI teams, and business stakeholders as the organization expands its enterprise data capabilities.

Responsibilities

 * Design, build, and maintain scalable ETL/ELT data pipelines.
 * Integrate structured and unstructured data from multiple enterprise systems.
 * Develop and optimize data models used for analytics and reporting.
 * Build solutions within cloud data environments such as Azure, AWS, or GCP.
 * Monitor data pipelines for performance, reliability, and data quality.
 * Partner with analysts, architects, and application teams to understand data requirements.
 * Support the migration of legacy data workloads to modern cloud platforms.
 * Develop reusable frameworks and automation for data processing.