Hadoop Data Engineer

Qode•Texas, TX

About The Position

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.

Requirements

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
Understanding of banking and financial services data
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

Nice To Haves

Knowledge of cloud data platforms (AWS EMR, Azure Data Lake) is a plus
Exposure to risk, compliance, or fraud analytics is preferred

Responsibilities

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.