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Huntly ● For Employers ● Hire Big Data&Analytics Engineers ● Hire Hadoop Developers

Hire Hadoop Developers

Struggling to manage massive datasets? With an expert Hadoop developer on your core team, you’re bound to get the most out of this powerful open-source framework for data processing.

Leverage the vast Huntly network of global tech recruiters to find that perfect Hadoop programmer for your Big Data & Analytics needs.

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Recommended Сandidates

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​​Alice Jackson Hadoop Developer An experienced Hadoop developer with a strong foundation in software engineering and a keen interest in big data analytics; proficient in designing and implementing scalable data processing solutions leveraging the Hadoop ecosystem.
5 years of experience
USA
Java, Hadoop, Apache Spark
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​​Alice Jackson Hadoop Developer An experienced Hadoop developer with a strong foundation in software engineering and a keen interest in big data analytics; proficient in designing and implementing scalable data processing solutions leveraging the Hadoop ecosystem.
5 years of experience
USA
Java, Hadoop, Apache Spark
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Bob Garcia Hadoop Developer A proficient Hadoop developer with a background in computer science; skilled in developing and deploying end-to-end big data solutions using the power of data modeling and optimization to ensure high-performing data processing pipelines.
12 years of experience
Europe
Python, Hadoop, AWS
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Bob Garcia Hadoop Developer A proficient Hadoop developer with a background in computer science; skilled in developing and deploying end-to-end big data solutions using the power of data modeling and optimization to ensure high-performing data processing pipelines.
12 years of experience
Europe
Python, Hadoop, AWS
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Understanding the Role of Hadoop Experts

Hadoop developers are Big Data specialists. Their roles encompass a multitude of responsibilities, which include:

  • designing and developing Hadoop architectures: Hadoop developers design data storage solutions using HDFS, configure YARN for resource management, and build data processing pipelines using MapReduce or Spark;
  • implementing efficient data pipelines: they design data ingestion, transformation, and delivery processes to move data through the Hadoop ecosystem;
  • troubleshooting and optimizing Hadoop clusters: Hadoop engineers monitor cluster performance, troubleshoot issues, and implement optimization techniques;
  • staying current with the latest advancements: top professionals stay up-to-date with new tools and emerging technologies in Big Data;
  • collaborating with stakeholders: Hadoop developers work with data analysts, data scientists, and others to understand business needs and translate those needs into technical solutions.

In short, Hadoop for developers is the bridge between raw data and actionable insights.

Hire Hadoop Developers: What to Look For

When hiring Hadoop developers, look for candidates with:

  • strong programming skills in Java, Python, or Scala. Experience in developing MapReduce jobs and writing efficient data processing logic is a plus.
  • in-depth knowledge of Hadoop core components including HDFS (Hadoop Distributed File System) for storing large datasets across clusters, YARN (Yet Another Resource Negotiator) for managing resources and job scheduling within the Hadoop cluster, and MapReduce, the programming framework for processing large datasets in parallel.
  • understanding of data warehousing and data modeling concepts to design data structures and schema to efficiently store and retrieve data for analysis. Familiarity with data warehousing principles like dimensional modeling is beneficial.
  • experience with distributed computing concepts like fault tolerance, scalability, and data partitioning that are crucial for designing and maintaining robust Hadoop applications.

How it Works

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What Needs is Huntly Best For?

Hiring Top TalentHuntly leverages our global network to find the perfect Hadoop developer for your needs. With thousands of tech recruiters onboard, we literally have an unlimited candidate pool.
Saving up Your ResourcesWhen you don’t have time or resources to hire tech specialists on your own, you can delegate this task to Huntly and focus on crucial business needs.
Scaling Your TeamIf your company is growing rapidly, you need to attract the best talent in the shortest time possible. At Huntly, we can provide you with the first relevant candidates within 72 hours after posting a job.
Recruitment has never been so safe
Relevant candidates only4 out of 5 candidates we send receive an interview invitation from the client.
Three months to reviewEnjoy ample time to assess candidates throughout the standard probation period.
Money back guaranteeA risk-free experience aligned with our refund policy.

FAQ

What is Hadoop? Hadoop, also known as Apache Hadoop, is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware.
Why do I need to hire Hadoop developers? A developer of Hadoop programs is a true asset for organizations working with Big Data. They design and implement solutions to manage, store, and analyze massive datasets, unlocking valuable insights.
What skills should I look for in Hadoop developers? Look for strong programming skills (Java, Python, Scala), experience with HDFS, YARN, MapReduce, and knowledge of data warehousing and distributed computing concepts.
How can I assess the proficiency of Hadoop developers? To evaluate Hadoop experts’ skills and experience, take advantage of technical assessments and scenario-based questions.
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Top Interview Questions to Ask When Hiring Skilled Hadoop Developers

The questions we collected can help you evaluate both theoretical knowledge and practical experiences of Hadoop candidates. Here's a curated list of top interview questions tailored for identifying skilled Hadoop developers who can navigate the complexities of Big Data:

  • Can you explain the architecture of a Hadoop cluster and role of each component?
  • What is HDFS and how does it handle data storage and replication in a distributed environment?
  • Describe the purpose of YARN and how it manages resources in a Hadoop cluster.
  • Differentiate between MapReduce and Apache Spark. When would you choose one over the other?
  • How do you handle data skewness in a MapReduce job?
  • Can you explain the concept of partitioning in Hadoop? How does it contribute to performance optimization?
  • What are the key challenges in optimizing Hadoop cluster performance, and how would you address them?
  • Discuss fault tolerance mechanisms in Hadoop and how they ensure data reliability.
  • How do you design an efficient data ingestion process in Hadoop, considering various data formats and sources?
  • Describe a scenario where you had to troubleshoot and resolve performance issues in a Hadoop cluster. What was your approach?
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