Location: Phoenix, AZ
Salary: $38.00 USD Hourly - $41.00 USD Hourly
Description: Data Engineer - Google Cloud Platform (Contingent Assignment)We are not accepting C2C or 1099 arrangements.Location: Phoenix, AZ
Employment Type: Contingent / Contract
Experience Level: Mid-Senior (5+ Years)
About the RoleAs a Data Engineer, you will design, develop, and optimize scalable data platforms and streaming solutions that support enterprise analytics, machine learning, and business intelligence initiatives. You will work within a highly collaborative engineering environment to build cloud-native data solutions, modernize legacy platforms, and improve data processing reliability, performance, and governance.
You will contribute to the design and implementation of high-volume data pipelines, real-time streaming frameworks, and lakehouse architectures using Google Cloud technologies and open-source data engineering tools.
Responsibilities- Design, develop, and maintain scalable batch and real-time data pipelines using Spark, Kafka, and Flink.
- Build and support cloud-native data platforms on Google Cloud Platform (Google Cloud Platform), including BigQuery, Cloud Storage, Dataproc, and Cloud Composer.
- Develop and optimize data processing workflows using Python, PySpark, SQL, and related technologies.
- Implement modern data lakehouse architectures leveraging technologies such as Iceberg, Delta Lake, and Parquet.
- Collaborate with software engineers, architects, product teams, and business stakeholders to deliver reliable data solutions.
- Perform data migration and modernization initiatives from on-premises environments to cloud-native platforms.
- Improve data quality, governance, observability, and operational efficiency through automation and engineering best practices.
- Support CI/CD deployment processes and infrastructure automation for data platforms.
- Participate in code reviews, technical discussions, and continuous improvement initiatives.
- Research and evaluate new technologies and recommend solutions for complex data engineering challenges.
Minimum Qualifications- Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent practical experience.
- 5+ years of experience in Data Engineering or Software Engineering.
- 5+ years of hands-on experience with Hadoop ecosystems and Google Cloud data solutions.
- Experience building and supporting distributed data processing solutions using Apache Spark.
- 2+ years of experience developing streaming data solutions using Kafka, Flink, and Spark Streaming.
- 3+ years of experience designing and implementing data lakehouse architectures.
- Experience with:
- Python and PySpark
- Apache Kafka
- Apache Airflow
- SQL
- Google Cloud Storage
- BigQuery
- Dataproc
- Cloud Composer
- Experience working with NoSQL databases, including columnar, graph, document, and key-value databases.
- Strong understanding of scalable distributed computing and data engineering best practices.
Preferred Qualifications- Google Cloud Professional Data Engineer certification or equivalent cloud certification (AWS Specialty Data Analytics or Azure Data Engineer).
- Experience migrating large-scale data platforms from on-premises environments to Google Cloud.
- Strong knowledge of Hadoop ecosystem technologies, including:
- Hive
- HDFS
- Parquet
- Apache Iceberg
- Delta Lake
- Deep understanding of data warehouse architecture, cloud data platforms, data orchestration, and pipeline optimization.
- Experience designing highly scalable, modular, and governance-driven data frameworks.
- Knowledge of Generative AI frameworks such as LangChain and LangGraph for agent-based data applications.
- Experience with DevOps and CI/CD practices, including Git, Jenkins, Docker, and Kubernetes.
- Familiarity with React and Node.js development for data-focused web applications.
What You'll Bring- Strong problem-solving and analytical skills.
- Ability to work effectively in a fast-paced, highly collaborative environment.
- Excellent communication and stakeholder management capabilities.
- Passion for modern data architectures, cloud technologies, and continuous learning.
Work Location: Phoenix, Arizona (Hybrid/Onsite as required)
Assignment Type: Contingent Workforce Assignment
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