Arkhya Tech. Inc.
Hadoop Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Hadoop Data Engineer with 8–12 years of experience in Java, Spring Boot, Apache Spark, Scala, Hadoop, and Kafka. Contract length is unspecified, and the pay rate is competitive. Remote work is allowed.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
July 17, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Scottsdale, AZ
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🧠 - Skills detailed
#GIT #Spring Boot #Version Control #DevOps #Data Engineering #Data Architecture #Kafka (Apache Kafka) #Spark (Apache Spark) #SQL (Structured Query Language) #Web Services #Maven #Apache Spark #GitLab #Big Data #Hadoop #Scala #Java
Role description
Required Skills & Qualifications
• 8–12 years of hands-on experience in Java (version 8 or above).
• Strong expertise in Spring Boot framework.
• Excellent proficiency in Apache Spark and Scala.
• Strong experience with Hive, SQL optimization, Hadoop, and Kafka.
• Solid understanding of RESTful web services.
• Experience with version control systems such as Git / GitLab.
• Hands-on experience with build tools like Maven and/or Gradle.
• Strong understanding of distributed systems and big data architecture.
• Experience with streaming frameworks such as Spark Streaming and Kafka Streams.
• Experience working in large-scale enterprise data platforms.
• Exposure to performance tuning and capacity planning for big data systems.
• Knowledge of DevOps or CI/CD pipelines is a plus.
Required Skills & Qualifications
• 8–12 years of hands-on experience in Java (version 8 or above).
• Strong expertise in Spring Boot framework.
• Excellent proficiency in Apache Spark and Scala.
• Strong experience with Hive, SQL optimization, Hadoop, and Kafka.
• Solid understanding of RESTful web services.
• Experience with version control systems such as Git / GitLab.
• Hands-on experience with build tools like Maven and/or Gradle.
• Strong understanding of distributed systems and big data architecture.
• Experience with streaming frameworks such as Spark Streaming and Kafka Streams.
• Experience working in large-scale enterprise data platforms.
• Exposure to performance tuning and capacity planning for big data systems.
• Knowledge of DevOps or CI/CD pipelines is a plus.
