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Data Operations Engineer

NsightUnited States🇺🇸United StatesPosted 8 Aug 2026

Why This Role Stands Out

This hybrid Data Operations Engineer role at Nsight offers a fantastic opportunity to enhance your skills in building and optimizing scalable data pipelines within a dynamic tech environment, while enjoying flexibility. If you thrive on ensuring system reliability and enjoy collaborating with engineering teams on cutting-edge data infrastructure, this is your chance to make a significant impact. Apply today to join a forward-thinking company and advance your career in data operations!

Quick Overview

Work Type
Hybrid
Level
Mid Senior

Job Description

About the role:

Looking for a Data Operations Engineer to support and optimize data infrastructure and pipeline operations across high-volume, distributed systems. This role focuses on ensuring reliability, scalability, and performance of data workflows while collaborating with engineering teams to maintain production-grade systems.
Key Responsibilities
Build, maintain, and optimize scalable data pipelines for high-throughput applications
Support operational health of data systems, ensuring reliability and uptime
Develop and enhance backend services using C# and Python
Manage and query large datasets in Snowflake and Google BigQuery environments
Deploy and maintain containerized applications using Docker in Linux-based systems
Integrate and manage streaming/data messaging systems such as Kafka (Confluent) and RabbitMQ
Monitor pipeline performance, troubleshoot issues, and implement improvements
Collaborate with cross-functional teams to ensure smooth data operations and deployments
Required Qualifications
5+ years of experience in infrastructure and software engineering with exposure to data engineering and pipelines
Proficiency in C# and Python
Hands-on experience with Snowflake and/or Google BigQuery
Strong experience working in Linux/Unix environments and Docker
Experience building and maintaining data pipelines
Familiarity with distributed messaging systems such as Confluent Kafka or RabbitMQ
Ability to work independently in a remote environment while communicating effectively
Preferred Qualifications
Experience with Elastic/ELK stack for logging and monitoring
Familiarity with Terraform for infrastructure as code
Experience with Kubernetes for container orchestration
What Success Looks Like
Reliable, efficient data pipeline operations with minimal downtime
Improved observability and monitoring of data systems
Timely resolution of production issues and performance bottlenecks
Well-documented systems and processes for maintainability
Work Environment
Fast-paced, engineering-driven culture
Opportunity to work on modern data platforms and distributed systems

Skills

Docker
ELK
Snowflake
BigQuery
C#
Data Pipeline
Kafka
Kubernetes
Python
RabbitMQ
Terraform

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