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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer, AI & Data Platforms - **Company:** THE JUDGE GROUP, INC. - **Location:** Phoenix, AZ, United States - **Experience:** Expert - **Salary:** $108,160.0 - $118,560.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Applicant Tracking Systems, BigQuery, Cloud Computing, Cloud Storage, Information Systems, Information Engineering, Data Governance, Data Systems, Data Intelligence, Python (Programming Language), Metadata, Meta-Data Management, Cloud Services, Cloudera, Data Streaming, Google Cloud, Cloud Platform System, Data Ingestion, Apache Spark, Generative AI, Pyspark, Information Technology, Apache Flink, Apache Kafka, Spark Streaming, Data Management, Data Lakehouse, Virtual Agents, Data Pipelines - **Published:** July 31, 2026 - **Apply:** https://www.dice.com/job-detail/ecc4be7b-4a25-45d7-822f-ca57ec99e31f ## About the Role * Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent practical experience. * 5+ years of experience in Data Engineering with hands-on development and support of cloud-based data platforms. * 5-6 years of experience working with Google Cloud Platform (Google Cloud Platform). * Experience building and supporting data ingestion and processing pipelines using Apache Spark and PySpark. * 3+ years of experience designing and implementing Data Lakehouse architectures. * Proficiency in: + Python + PySpark + Kafka + Apache Airflow + Google Cloud Storage (GCS) + BigQuery + Dataproc + Cloud Composer * Experience developing real-time and streaming data solutions using: + Kafka + Apache Flink + Spark Streaming * Recent hands-on experience with AI-enabled development frameworks and tools. Preferred Qualifications * 6-12 months of practical experience with modern AI and Agentic AI technologies. * Experience building intelligent data solutions using: + LangChain + LangGraph + Agent Development Kit (ADK) + Agentic Frameworks + Retrieval-Augmented Generation (RAG) + GraphRAG + Model Context Protocol (MCP) * Knowledge of data governance, data quality, metadata management, and compliance automation. * Strong problem-solving skills and ability to work within complex, enterprise-scale environments. * Experience collaborating across engineering, product, and architecture teams. Key Technologies Cloud: Google Cloud Platform (Google Cloud Platform), BigQuery, Dataproc, Cloud Composer, Google Cloud Storage Data Engineering: Python, PySpark, Apache Spark, Kafka, Flink, Airflow, Data Lakehouse AI & Agentic Technologies: LangChain, LangGraph, ADK, RAG, GraphRAG, MCP, Agent-Based Architectures Must-Have Skills Summary * 5-6 years of Google Cloud Platform (Google Cloud Platform) experience * 5+ years of Data Engineering experience * Data Lakehouse architecture and implementation * Python, PySpark, Spark, Kafka, Airflow * BigQuery, Dataproc, Cloud Composer, Cloud Storage * Kafka, Flink, and Spark Streaming * Hands-on exposure to AI/Agentic frameworks (LangChain, LangGraph, RAG, GraphRAG, MCP) This version is optimized for posting on LinkedIn, Google-style technical recruiting, and ATS systems. ## Description We are seeking a highly skilled Senior Data Engineer with expertise in Google Cloud Platform (Google Cloud Platform) and emerging AI/Agentic technologies to build and scale next-generation data platforms. In this role, you will design and operationalize AI-enabled data solutions that support large-scale analytics, data governance, and intelligent data consumption across the enterprise. You will partner with principal engineers, product managers, architects, and data engineering teams in a matrixed environment to deliver innovative, cloud-native data capabilities that drive business value. What You'll Do * Design, implement, and support modern AI-enabled data platforms on Google Cloud. * Build scalable data ingestion, transformation, and distribution frameworks for large-scale analytics and data applications. * Develop and operationalize cloud-native data pipelines using Spark, Kafka, Flink, and related technologies. * Leverage AI and agentic frameworks to automate data management, governance, quality, metadata, lineage, and compliance processes. * Create intelligent data capabilities utilizing Retrieval-Augmented Generation (RAG), GraphRAG, and agent-based architectures. * Collaborate with cross-functional stakeholders to define technical roadmaps and deliver prioritized data solutions. * Improve platform reliability, performance, scalability, and observability across data ecosystems. * Support data lakehouse architecture initiatives and enterprise data modernization efforts. * Apply engineering best practices for security, governance, and regulatory compliance. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) ## Related Articles - [Got AI ideas but no money? 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