Big Data Developer

  • Charlotte, NC
  • Posted 16 hours ago | Updated 14 hours ago

Overview

Hybrid
Depends on Experience
Contract - Independent
Contract - W2
Contract - 12 Month(s)

Skills

Amazon Web Services
Apache Hadoop
Apache Hive
Apache Kafka
Artificial Intelligence
Batch Processing
Big Data
Cloud Computing
Cloudera
Collaboration
Communication
Conflict Resolution
Google Cloud Platform
Machine Learning (ML)
Management
Data Engineering
Data Flow
Performance Monitoring
Problem Solving
Django
Flask
Good Clinical Practice
Hortonworks
Microsoft Azure
PySpark
Python
RESTful
Scala
Scalability
Streaming
Use Cases

Job Details

Position: Big Data Developer

Location: Charlotte, NC (Hybrid Model)

Job Type: 6-24 Months Contract to hire FTE

Note: Only W2 (No C2C)

Required Skills & Qualifications

  • Experience in Big Data Engineering.
  • Strong hands-on experience with PySpark or Scala.
  • Deep expertise in on-prem Hadoop distributions (Cloudera, Hortonworks, MapR) or cloud-based big data platforms.
  • Proficiency in Kafka batch processing, Hive, and Dremio.
  • Solid understanding of REST API development using Python frameworks.
  • Familiarity with cloud platforms (Google Cloud Platform, AWS, or Azure).
  • Experience or exposure to AI and RAG architectures is a plus.
  • Excellent problem-solving, communication, and collaboration skills.

Key Responsibilities:

  • Design and implement scalable data ingestion and transformation pipelines using PySpark or Scala, Hadoop, Hive, and Dremio.
  • Build and manage Kafka batch pipelines for reliable data streaming and integration.
  • Work with on-prem Hadoop ecosystems (Cloudera, Hortonworks, MapR) or cloud-native big data platforms.
  • Develop and maintain RESTful APIs using Python (FastAPI, Flask, or Django) to expose data and services.
  • Collaborate with data scientists, ML engineers, and platform teams to ensure seamless data flow and system performance.
  • Monitor, troubleshoot, and optimize production data pipelines and services.
  • Ensure security, scalability, and reliability across all data engineering components.
  • (Optional but valuable) Contribute to the design and deployment of AI-driven RAG systems for enterprise use cases.
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