# DataOps Engineer

**Company:** [Panopto](https://hotfix.jobs/companies/panopto)
**Location:** Remote
**Role:** DevOps / SRE
**Salary:** $140k – $150k/yr
**Experience:** 5+ years
**Skills:** AWS, Ms Sql Server, Python, Bash, PowerShell, C#, CI/CD, Infrastructure As Code, CloudWatch, EC2, S3
**Posted:** 2026-03-13

> DataOps Engineer architects automated data pipelines and "Golden Path" for operational, analytics, and AI data layers in AWS-hosted SQL Server environment. Requires 5+ years DevOps/DBA experience, AWS/SQL expertise, and automation scripting proficiency.

## Job Description

## Responsibilities

- Engineer the Data Lifecycle: Design and implement the "Golden Path" for data, ensuring seamless transitions between operational SQL environments, analytics warehouses, and AI-ready data sets.
- Implement Data as Code: Move beyond manual administration by treating AWS-hosted MS SQL infrastructure as a version-controlled, automated ecosystem using CI/CD and Infrastructure as Code (IaC).
- Architect Multi-Layer Reliability: Build frameworks that guarantee data quality and availability across all tiers—from high-concurrency operational databases to complex feature stores used by AI and Machine Learning models.
- Optimize for Scalability & Performance: Identify and resolve architectural bottlenecks in massive SQL Server environment, ensuring high-throughput demands of modern SaaS analytics.
- Standardize Data Observability: Develop advanced monitoring and alerting strategies for deep visibility into data health.
- Bridge the Engineering Gap: Collaborate with Software Engineers and Data Scientists to support rapid product iteration and long-term research initiatives.

## Requirements

- 5+ years of experience in DevOps or Database Administration, with passion for migrating legacy data systems into modern, automated workflows.
- Deep expertise in managing MS SQL Server on AWS infrastructure (EC2, S3, CloudWatch).
- Proficient in Python, Bash, or PowerShell and working knowledge of C# for automation scripts.
- Outcome-oriented with ownership mindset.

## Success Metrics

- Within 6 Months: Complete audit of data deployment processes and identify top three bottlenecks.
- Within 1 Year: Implement automated CI/CD for database schema changes, reducing deployment downtime.
- Long-term: Own "Data as Code" strategy for agile and resilient data layer.

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