Senior Big Data Engineer (Kubernetes / AWS / Spark) || In person interview || DC/VA/MD/NY/NJ

Nexiva Inc

Rockville, MD, United States

about 1 month ago

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Compensation

$131,300.0 - $237,350.0

Working hours

Regular working hours

Job source

www.careerjet.com

Tech stack

Artificial Intelligence Amazon Web Services Amazon Elastic Compute Cloud Big Data Cloud Engineering Program Optimization Continuous Integration DevOps Distributed Systems Elasticsearch Python (Programming Language) Open Source Technology

+15 more

Requirements Management Scala (Programming Language) SQL Databases Data Processing Google Cloud Apache Spark Generative AI Gitlab Containerization Kubernetes Optimization Algorithms Bitbucket Data Management Data Pipelines Serverless Computing

Job description

Design, develop, and optimize data pipelines handling terabyte-scale datasets
Work with complex algorithms to process and analyze large volumes of data efficiently
Optimize code performance for scalability and high-throughput systems
Build and maintain containerized, serverless data platforms using Kubernetes
Support migration efforts from EMR/EC2-based systems to Kubernetes-based architecture
Maintain and enhance existing Kubernetes infrastructure
Develop and manage CI/CD pipelines using tools like GitLab and Bitbucket
Collaborate on requirements documentation, system design, and implementation
Contribute to emerging initiatives involving:
GenAI integration
AI agents and automation frameworks
Technologies such as Kiro (Amazon GenAI) and MCP (Model Context Protocol)

Technical Environment

Cloud Platforms: AWS (current), with exposure to Google Cloud and open-source ecosystems
Core Technologies:
Kubernetes (primary focus)
Elasticsearch
Big Data frameworks (EMR, distributed systems)
Programming Languages: Python, SQL, Scala (flexible)
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Requirements

Strong experience with Kubernetes, including deployment, migration, and infrastructure management
Experience working with large-scale data (terabytes) and distributed systems
Proficiency in at least one: Python, SQL, or Scala
Understanding of data processing optimization techniques
Familiarity with cloud-native architectures and containerization
Experience with CI/CD pipelines and modern DevOps practices
Exposure to AI/GenAI tools, agents, or related frameworks is a strong plus
AWS and/or Kubernetes certifications preferred