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Entry Level Hadoop Developer Jobs (NOW HIRING)

Noblesoft Solutions

Data Scientist

Jacksonville, FL · On-site

This is an entry level Data Scientist Role for a person who is self motivated and has a passion to ... Experience in a Hadoop environment and / or statistical development tool (e.g. RapidMiner, Knime) a ...

Noblesoft Solutions

Data Scientist

Jacksonville, FL · On-site

This is an entry level Data Scientist Role for a person who is self motivated and has a passion to ... Experience in a Hadoop environment and / or statistical development tool (e.g. RapidMiner, Knime) a ...

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How much do entry level hadoop developer jobs pay per hour?

As of Sep 4, 2026, the average hourly pay for entry level hadoop developer in the United States is $59.89, according to ZipRecruiter salary data. Most workers in this role earn between $56.97 and $65.38 per hour, depending on experience, location, and employer.

What is an entry level Hadoop developer?

An Entry Level Hadoop Developer is responsible for developing, maintaining, and managing big data applications using Hadoop and related technologies. They assist in writing MapReduce programs, managing Hadoop clusters, and optimizing data processing workflows. Typically, they work under the guidance of senior developers to ensure efficient data storage, retrieval, and processing. Strong knowledge of Hadoop ecosystems like HDFS, Hive, Pig, and Spark is essential. This role serves as a foundation for building expertise in big data engineering and analytics.

What does an entry level Hadoop developer do?

As an Entry Level Hadoop Developer, you will commonly be responsible for developing, testing, and troubleshooting big data applications using Hadoop ecosystem tools under the guidance of senior engineers. You’ll handle tasks such as loading data, writing simple MapReduce jobs, and maintaining data pipelines, often collaborating with data analysts, data scientists, and database administrators. Daily responsibilities may include monitoring Hadoop clusters, addressing performance issues, and assisting with basic data integration projects. The role provides valuable on-the-job training, and as you gain experience, you may take on more complex projects and grow into advanced developer or data engineering roles.

What are the key skills and qualifications needed to thrive as an entry level Hadoop developer?

To thrive as an Entry Level Hadoop Developer, you need a solid understanding of Java or Python programming, basic knowledge of big data concepts, and a bachelor’s degree in computer science or a related field. Familiarity with Hadoop ecosystem tools such as HDFS, MapReduce, Pig, and Hive, as well as relevant certifications like Cloudera Certified Associate (CCA), is highly beneficial. Strong problem-solving skills, attention to detail, and the ability to collaborate effectively with team members will set you apart. These skills ensure that you can efficiently manage, process, and analyze large datasets while working productively within a data engineering team.

How to become an entry level Hadoop developer?

To become an entry-level Hadoop developer, you should gain a solid understanding of big data concepts and learn Hadoop ecosystem components such as HDFS, MapReduce, and YARN. Acquiring programming skills in Java or Python, completing relevant training or certifications, and gaining hands-on experience with Hadoop tools are essential steps to enter this role.
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Entry Level Hadoop Developer jobs near you
Infographic showing various Entry Level Hadoop Developer job openings in the United States as of August 2026, with employment types broken down into 85% Full Time, 3% Part Time, and 12% Contract. Highlights an 80% Physical, 5% Hybrid, and 15% Remote job distribution, with an average salary of $124,575 per year, or $59.9 per hour.

Data Scientist - Fraud Detection

DataVisor

Mountain View, CA • On-site

Full-time

Medical, PTO

Posted 8 days ago

Key responsibilities

  • Develop and deploy machine learning models for fraud detection and risk assessment.

  • Perform exploratory data analysis (EDA) to identify trends, anomalies, and patterns in transactional data.

  • Collaborate with engineering and business teams to integrate ML models into production systems.

Job description

About DataVisor:
DataVisor is the world’s leading AI-powered Fraud and Risk Platform that delivers the best overall detection coverage in the industry. With an open SaaS platform that supports easy consolidation and enrichment of any data, DataVisor's fraud and anti-money laundering (AML) solutions scale infinitely and enable organizations to act on fast-evolving fraud and money laundering activities in real time. Its patented unsupervised machine learning technology, advanced device intelligence, powerful decision engine, and investigation tools work together to provide significant performance lift from day one. DataVisor's platform is architected to support multiple use cases across different business units flexibly, dramatically lowering total cost of ownership, compared to legacy point solutions. DataVisor is recognized as an industry leader and has been adopted by many Fortune 500 companies across the globe.

Our award-winning software platform is powered by a team of world-class experts in big data, machine learning, security, and scalable infrastructure. Our culture is open, positive, collaborative, and results-driven. Come join us!

Position Overview:

We are looking for a motivated Entry-Level Data Scientist to join our Fraud Detection team. In this role, you will leverage your machine learning and data analysis skills to identify fraudulent activities, build predictive models, and uncover hidden patterns in large datasets. You will work closely with cross-functional teams to develop scalable solutions that enhance our fraud detection capabilities. This is a great opportunity to grow your skills in a fast-paced, data-driven environment while making a real impact in the fight against fraud.

Key Responsibilities:

  • Develop and deploy machine learning models for fraud detection and risk assessment.
  • Perform exploratory data analysis (EDA) to identify trends, anomalies, and patterns in transactional data.
  • Clean, preprocess, and analyze large datasets using Python and popular data science libraries (pandas, NumPy, scikit-learn, etc.).
  • Collaborate with engineering and business teams to integrate ML models into production systems.
  • Continuously monitor model performance and refine algorithms to improve accuracy.
  • Stay updated with the latest advancements in fraud detection techniques and ML/AI technologies.

Requirements

  • Master’s degree in Computer Science, Data Science, Statistics, or a related quantitative field. Ph.D. degree is a plus. 
  • Strong programming skills in Python and familiarity with data science libraries (NumPy, Pandas, scikit-learn, TensorFlow/PyTorch is a plus).
  • Solid understanding of machine learning algorithms (supervised/unsupervised learning, anomaly detection, classification, etc.).
  • Experience with SQL and data manipulation/analysis in large datasets.
  • Strong problem-solving skills and patience for deep-dive data exploration.
  • Prior internship or project experience in fraud modeling, risk analysis, or related fields is a plus.
  • Excellent communication skills and ability to work in a collaborative environment.
Nice to have
  • Familiarity with big data tools (Spark, Hadoop, Dask).
  • Knowledge of graph-based fraud detection techniques.
  • Experience with cloud platforms (AWS, GCP, Azure).

Benefits

PTO, Stock Option, Health Benefits