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# Hadoop Data Engineer

- **Company:** Qode LLC
- **Location:** Pittsburgh, PA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Airflow, Amazon Web Services, Microsoft Azure, Big Data, Unix, Computer Programming, Information Engineering, Extract Transform Load (ETL), Data Security, Data Systems, Data Warehousing, Apache Hadoop, Hadoop Distributed File System, MapReduce, Apache HBase, Apache Hive, Python (Programming Language), NoSQL, Apache Oozie, Performance Tuning, Scrum Methodology, Cloud Services, Azure Data Lake, SQL Databases, Sqoop, Data Streaming, Talend, Unstructured Data, Workflow Management Systems, Data Ingestion, Apache Spark, Information Technology, Integration Frameworks, Apache Kafka, Data Management, Data Pipelines, Amazon Elastic Mapreduce (EMR)
- **Published:** June 20, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=322f03220ffc005f

## About the Role

Do you have experience in UNIX?, Do you have a Master's degree?, * Strong experience with:

 * Hadoop ecosystem (HDFS, MapReduce, Hive, HBase)
 * Apache Spark (Scala/Python)
 * SQL & NoSQL databases
 * ETL tools (Informatica, Talend, or similar)
 * Kafka or other streaming tools
 * Proficiency in programming:
 * Python / Java / Scala
 * Experience with:
 * Data warehousing concepts
 * Workflow orchestration tools (Airflow, Oozie)
 * Unix/Linux environments
 * Knowledge of cloud data platforms (AWS EMR, Azure Data Lake) is a plus

Domain Knowledge

 * Understanding of banking and financial services data
 * Exposure to risk, compliance, or fraud analytics is preferred

Soft Skills

 * Strong problem-solving and analytical abilities
 * Excellent communication and collaboration skills
 * Ability to work in Agile/Scrum environments, * Bachelor's or Master's degree in:
 * Computer Science, Information Technology, or related field
 * Typically 5-10 years of experience in data engineering or big data development

## Description

Hadoop Data Engineer responsible for designing, developing, and maintaining large-scale data processing systems within a distributed Hadoop ecosystem. The role focuses on enabling data-driven decision-making across banking operations, risk management, compliance, and customer analytics., * Design, develop, and maintain scalable data pipelines using Hadoop ecosystem tools (HDFS, Hive, Spark, Sqoop, Kafka).

 * Build and optimize ETL/ELT processes to support data ingestion from multiple banking systems.
 * Develop and manage big data solutions for structured and unstructured data.
 * Collaborate with data analysts, data scientists, and business stakeholders to deliver data solutions.
 * Ensure data quality, integrity, and governance aligned with banking and regulatory standards.
 * Perform performance tuning and optimization of Hadoop/Spark jobs.
 * Implement data security controls to comply with financial regulations (e.g., PCI, SOX).
 * Support real-time and batch data processing frameworks.
 * Troubleshoot production issues and provide continuous support for data platforms.
 * Work with cloud platforms (e.g., AWS, Azure) for modern data solutions.


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