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# Data & Analytics (Hadoop + Big Data)

- **Company:** Q1 Technologies, Inc.
- **Location:** Charlotte, NC, United States
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Data Analysis, Computing Platforms, Big Data, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Data Security, Data Warehousing, Distributed Computing Environment, Apache Hadoop, Hadoop Distributed File System, MapReduce, Apache HBase, Apache Hive, Python (Programming Language), Kerberos (Protocol), Meta-Data Management, DataOps, Scala (Programming Language), SQL Databases, Data Streaming, Enterprise Data Management, Scripting, Data Ingestion, Apache Yarn, System Availability, Apache Spark, Data Lakes, Pyspark, Data Analytics, Apache Kafka, Data Management
- **Published:** November 10, 2026
- **Apply:** https://www.careerbuilder.com/job-details/data-analytics-hadoop-big-data-charlotte-nc--09c8c97c-2922-4c11-a4f0-fef33795b685

## About the Role

Apache Hadoop, Big Data, Data Analysis, Apache Hadoop, Ecosystems, Big Data, Enterprise Architecture, HDFS (Hadoop Distributed File System), Apache Spark, Python Programming/Scripting Language, · 14+ years of experience in Enterprise Data Architecture, Hadoop Ecosystem, Big Data Engineering, and Data Platform Modernization.
· Strong hands-on expertise in Hadoop, HDFS, YARN, Hive, Spark, PySpark, Kafka, SQL, Python, Java, and Scala.
· Experience designing and implementing large-scale Big Data and Data Lake architectures supporting analytics, reporting, and AI workloads.
· Strong understanding of Hadoop platform architecture, including HDFS, YARN, high availability, cluster management, workload optimization, and scalability.
· Expertise in batch and distributed data processing using Spark, Hive, HBase, and MapReduce frameworks.
· Experience building data ingestion and streaming solutions using Kafka and Hadoop ecosystem tools.
· Strong knowledge of Data Engineering practices, including ETL/ELT, data integration, data quality, metadata management, lineage, observability, and DataOps.
· Experience with Data Lake, Data Warehousing, Enterprise Data Models, Master Data, and Data Governance frameworks. · Strong understanding of security and governance controls, including Kerberos, Ranger, encryption, privacy, auditability, compliance, and controlled data access.