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# Lead DataOps Engineer

- **Company:** Kforce Inc.
- **Location:** United States
- **Experience:** Expert
- **Contract:** Temporary to permanent
- **Skills:** Artificial Intelligence, Data Analysis, Microsoft Azure, Continuous Integration, Information Engineering, DevOps, Github, Machine Learning, DataOps, SQL Databases, Data Streaming, Gitlab, Pyspark, Machine Learning Operations, Stream Processing, Data Pipelines, Databricks
- **Published:** August 12, 2026
- **Apply:** https://www.dice.com/job-detail/253e33a7-4226-453b-96f2-d9e444a5fb24

## About the Role

* Experience in evaluating tooling, recommending processes, and designing the broader operating model
 * Expertise with client's core tools including Azure Databricks, Azure DevOps, GitHub, and data pipeline/orchestration tools
 * Data Operations expertise in a support role is required for this role
 * DataOps, enterprise data pipeline, Azure Databricks, SQL, PySpark, CI/CD, and DevOps experience
 * Proficient with CI/CD tools (Azure DevOps, GitHub Actions, GitLab, etc.)
 * Cross-functional leadership and technical credibility are critical

Preferred Experience:

 * SAFe Agile experience
 * Streaming platforms/real-time data processing
 * AI or machine learning workload support
 * FinOps/cloud cost governance
 * Insurance, financial services, or another regulated industry

## Description

Kforce has a client that is looking for a Lead DataOps Engineer to work on-site in Short Hills, NJ 2 days a week for a contract to hire opportunity., The Lead DataOps Engineer will work on projects responsible for technology operations and support of all Data Engineering, Analytics and Reporting for specific business areas. This includes Data Engineering services, Enterprise reporting support and ML Ops Engineering operations for these groups. We are looking for a DataOps leader who can build the function from the ground up, establish strategy and best practices, lead cross-functionally, and remain hands-on when needed. This role will coordinate with architects, developers, security, infrastructure, operations, BI, and data teams without necessarily having direct reports.


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