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# Data Engineer

- **Company:** THE JUDGE GROUP, INC.
- **Location:** Phoenix, AZ, United States
- **Experience:** Expert
- **Salary:** $79,040.0 - $85,280.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Apache HTTP Server, Big Data, BigQuery, Cloud Computing, Cloud Storage, Code Review, Information Systems, Databases, Continuous Integration, Data Architecture, Information Engineering, Data Migration, Data Systems, Data Warehousing, DevOps, Distributed Computing Environment, Distributed Systems, Apache Hadoop, Hadoop Distributed File System, Apache Hive, Python (Programming Language), Machine Learning, NoSQL, Cloud Services, DataOps, Cloudera, Software Engineering, SQL Databases, Data Streaming, Web Applications, Parquet, Data Processing, Google Cloud, Cloud Platform System, Azure Data Factory, ReactJS, Apache Spark, Generative AI, Git, Data Lakes, Pyspark, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Apache Flink, Data Analytics, Integration Frameworks, Apache Kafka, Spark Streaming, Data Management, Data Lakehouse, Data Pipelines, Docker, Legacy Systems, Jenkins
- **Published:** August 12, 2026
- **Apply:** https://www.dice.com/job-detail/86f4b56d-e1f2-4f5f-85e5-e9a7c2026a65

## About the Role

* 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., * 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.

## Description

As 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.


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