Specialty Software Engineer

Location: Phoenix, AZ Salary: $76.00 USD Hourly - $81.00 USD Hourly Description:

Senior Data Engineer, Cloud & Streaming Platforms (Contract)

We are not accepting C2C or 1099 arrangements.

Location: Phoenix, AZ
Employment Type: Contract / Contingent Workforce
Experience Level: Mid-Senior (5+ Years)
About the Role

We are seeking a highly skilled Senior Data Engineer to design, build, and optimize large-scale cloud-native data platforms and streaming solutions. In this role, you will partner with cross-functional engineering, architecture, and business teams to develop scalable data pipelines, modern lakehouse architectures, and real-time data processing capabilities that support enterprise analytics and AI initiatives.

The ideal candidate has deep expertise in Google Cloud Platform (Google Cloud Platform), big data technologies, streaming frameworks, and modern data engineering practices, along with a strong track record of delivering cloud migration and data modernization programs.
Responsibilities

• Design, develop, and maintain scalable data pipelines and streaming solutions using modern cloud-native technologies.
• Build and support real-time and batch data processing systems using Apache Spark, Kafka, Flink, and Python.
• Architect and implement enterprise data lakehouse solutions leveraging industry-standard data storage and governance practices.
• Develop and optimize cloud-based data solutions using Google Cloud Platform (Google Cloud Platform) services, including:
  • Big Query
  • Cloud Storage
  • Dataproc
  • Cloud Composer
• Lead data migration initiatives from on-premises environments to cloud-native architectures.
• Design and implement automated data quality, governance, monitoring, and security controls.
• Collaborate with architects, engineers, product teams, and stakeholders to deliver scalable and reliable data solutions.
• Build and maintain CI/CD pipelines and Dev Ops processes supporting data platform deployments.
• Evaluate and implement emerging technologies to improve data engineering capabilities and operational efficiency.
• Contribute to AI-enabled data solutions utilizing modern GenAI frameworks and agent-based architectures.

Minimum Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent practical experience.
• 5+ years of professional experience in Data Engineering or related software engineering disciplines.
• 5+ years of hands-on experience with Hadoop and cloud-based data platforms.
• 3+ years of experience designing and implementing data lakehouse architectures.
• 2+ years of hands-on experience developing streaming applications using:
  • Apache Kafka
  • Apache Flink
  • Spark Streaming
• Strong programming experience with:
  • Python
  • Py Spark
  • SQL
• Experience with Google Cloud Platform (Google Cloud Platform) services including Big Query, Cloud Storage, Dataproc, and Cloud Composer.
• Experience with No
  
  SQL technologies, including document, graph, key-value, and columnar databases.
• Strong understanding of data warehousing, distributed computing, and cloud data architecture.
• Experience with Hadoop ecosystem technologies such as:
  • Hive
  • HDFS
  • Parquet
  • Apache Iceberg
  • Delta Lake
• Experience implementing scalable, resilient, and highly available data platforms.

Preferred Qualifications

• Professional cloud certification such as:
  • Google Cloud Professional Data Engineer
  • AWS Specialty Data Analytics
  • Microsoft Azure Data Engineer Associate
• Experience with GenAI frameworks such as Lang Chain and Lang Graph.
• Experience with Dev Ops and CI/CD tools including:
  • Git
  • Jenkins
  • Docker
  • Kubernetes
• Experience developing web applications using React and Node.js.
• Strong communication, stakeholder management, and consulting skills.
• Experience working in highly collaborative Agile engineering environments.

Key Technologies

Cloud: Google Cloud Platform (Google Cloud Platform), Big Query, Cloud Storage, Dataproc, Cloud Composer

Data Engineering: Spark, PySpark, Kafka, Flink, Airflow, SQL

Big Data: Hadoop, Hive, HDFS, Parquet, Iceberg, Delta Lake

Databases: No

SQL, Columnar, Graph, Document, Key-Value Stores

Dev Ops: Git, Jenkins, Docker, Kubernetes

AI/ML: Lang Chain, Lang Graph

Frontend (Nice to Have): React, Node.js

This position offers the opportunity to work on large-scale enterprise data modernization initiatives, cloud transformation programs, and next-generation AI-driven data platforms.

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