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Hadoop Data Engineer Jobs (NOW HIRING)

Data engineer Location: Need to come in hybrid 3 times a week too Iselin, NJ Need to come onsite for final or an Apex Local office for final Needs: Hadoop AWS Spark PySpark Java Python Scala Job ...

5+ years of development and maintenance experience with Hadoop eco-system (Spark, Oozie, HDFS, Yarn, Hive) with experience on real-time & stream processing systems. Good knowledge of Java is needed ...

Hadoop Data Engineer

Dallas, TX · On-site

$113K - $136K/yr

Sr. Feature Engineer Dallas,TX/ Pittsburgh, PA / Clevland OH Tech: Data Engineering/Pipeline MLOps Engineering/Pipeline OpenShift Git Linux Programming language: Python SQL Spark Hive Title Skillsets ...

New

Hadoop Data Admin Location : Schaumburg, IL Type : Full Time Permanent Responsible for Data pipeline management, cross cluster data flows and data management. Responsible for Daily reporting of data ...

Job Summary : eTeam is seeking a Hadoop Spark Data Engineer to join their team. The role requires expertise in Hadoop, Spark, and Python for data migration projects and involves enterprise-level data ...

Big Data Engineer - Sr

Plano, TX · On-site

$53.50 - $71/hr

EMR/Hadoop, * Data Pipelines, Data Orchestration, Cloud Data Warehousing, Workflow Orchestration. * Agile Dexian stands at the forefront of Talent + Technology solutions with a presence spanning more ...

... data engineer. * Advanced knowledge of the Hadoop ecosystem and its components. * In-depth knowledge of Hive, HBase, and Pig. * Familiarity with MapReduce and Pig Latin Scripts. * Knowledge of ...

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How much do hadoop data engineer jobs pay per hour?

As of Aug 5, 2026, the average hourly pay for hadoop data engineer in the United States is $63.20, according to ZipRecruiter salary data. Most workers in this role earn between $55.29 and $73.80 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Hadoop data engineer, and why are they important?

To thrive as a Hadoop Data Engineer, you need strong programming skills (especially in Java, Python, or Scala), a solid understanding of distributed computing concepts, and a background in computer science or related fields. Familiarity with Hadoop ecosystem tools like HDFS, Hive, Pig, Spark, and workflow schedulers such as Oozie, as well as knowledge of big data certifications (e.g., Cloudera Certified Associate), are typically required. Analytical thinking, problem-solving, and effective communication are crucial soft skills for collaborating with cross-functional teams and translating business requirements into technical solutions. These skills and qualifications are essential for efficiently processing large datasets, ensuring data integrity, and delivering scalable data solutions that support organizational goals.

What is a Hadoop data engineer?

Hadoop Data Engineers are IT professionals who design, build, and maintain large-scale data processing systems using the Hadoop ecosystem. They are responsible for developing and managing complex data pipelines, ensuring the efficient storage, processing, and retrieval of big data. Their work involves using tools like HDFS, MapReduce, Hive, and Spark to process massive datasets, often in support of data analytics or machine learning initiatives. Hadoop Data Engineers also optimize data workflows, troubleshoot issues, and collaborate with data scientists and analysts to deliver reliable data solutions.

What are some typical challenges a Hadoop data engineer faces when managing large-scale data pipelines?

Hadoop Data Engineers often encounter challenges such as optimizing data processing jobs for performance, ensuring data integrity across distributed systems, and managing the scalability of clusters as data volumes grow. Troubleshooting job failures due to resource constraints or data inconsistencies is also common. Additionally, collaborating with data scientists and analysts to ensure data is transformed and made accessible in a timely manner is a key part of the role.

What is the difference between Hadoop Data Engineer vs Data Analyst?

AspectHadoop Data EngineerData Analyst
Required SkillsBig data tools, Hadoop ecosystem, SQL, Java/ScalaData visualization, SQL, Excel, statistical analysis
Work EnvironmentData engineering teams, cloud platforms, big data infrastructureBusiness units, reporting tools, data visualization platforms
CertificationsHadoop certifications, cloud certificationsNone specific, often data analysis or visualization certifications

The main difference between a Hadoop Data Engineer and a Data Analyst lies in their focus: Hadoop Data Engineers build and maintain big data infrastructure using Hadoop and related tools, while Data Analysts interpret data to generate insights using analysis and visualization tools. Both roles require strong SQL skills, but the engineering role emphasizes big data technologies and infrastructure development.

More about Hadoop Data Engineer jobs
What states have the most Hadoop Data Engineer jobs? States with the most job openings for Hadoop Data Engineer jobs include:
What job categories do people searching Hadoop Data Engineer jobs look for? The top searched job categories for Hadoop Data Engineer jobs are:
Infographic showing various Hadoop Data Engineer job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, 1% Temporary, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $131,458 per year, or $63.2 per hour.

Hadoop Data Engineer

Tror AI for everyone

Scottsdale, AZ • On-site

Contractor

Re-posted 14 days ago


Job description

Job Role: Hadoop Data Engineer

Job Location:  Scottsdale AZ (100%)

Job Type: Contract

Note: Need 10+ years of experience resumes.

Job Description:

  • Strong Experience in Scala, Spark, Hive SQl, Hadoop and Kafka
  • Proficiency in Hive and SQL optimization.
  • Understanding of distributed systems and big data architecture.
  • Knowledge of streaming frameworks (Spark Streaming, Kafka Streams).
  • Familiarity with data partitioning, compression, and serialization formats.