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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Operations Research / Data Platform Engineer - **Company:** Nexus - **Location:** Atlanta, GA, United States - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Data Analysis, Data Infrastructure, Data Systems, Identity and Access Management, Apache Spark, Information Technology, Data Pipelines - **Published:** July 4, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=b97cdecd475fee7f ## About the Role * Bachelor's degree in Computer Science, Engineering, Business Analytics, or a related field that develops analytical, logical, reasoning, and problem-solving skills. * Experience in the information technology industry applying quantitative and analytical methods to operational or data platform challenges. * Working knowledge of statistical analysis and mathematical modeling techniques as applied to data systems and platform performance evaluation. * Ability to translate quantitative findings into clear recommendations for both technical and non-technical stakeholders., * Master's degree in Business Analytics or a related discipline. * Experience transforming legacy infrastructure into scalable Spark/Airflow environments to support real-time analytical workloads. * Experience delivering data products that reduce operational latency and cost while enabling data-driven decision-making. * Familiarity with Infrastructure as Code practices supporting process optimization and automation., * A collaborative team culture built on curiosity and respect * Challenging work where your contributions clearly matter * A leadership team that invests in learning and development * The opportunity to work at the intersection of cloud, data, and AI innovation, If this role sounds like a great fit - or even close to one - we'd love to hear from you. We know that no candidate checks every single box, and we're excited to meet people who bring curiosity, talent, and a desire to build meaningful work together. ## Description * Formulate and apply mathematical and statistical models to evaluate the performance of NexusOne's cross-estate data orchestration layer, identifying optimization opportunities across identity management, governance enforcement, and data pipeline execution. * Define data requirements, gather and validate quantitative information from NexusOne's operational environment, and apply statistical methods to assess platform performance, pipeline throughput, and governance policy compliance across client deployments. * Present the results of mathematical modeling and data analysis to client stakeholders and delivery leadership, translating quantitative findings into platform configuration recommendations and operational decisions. * Collaborate with engineering and client delivery teams to identify and solve complex data operational problems - including legacy system modernization, AI pipeline readiness, and cross-estate governance gaps - using NexusOne as the enabling platform. * Prepare technical and management reports evaluating data operational problems, analyzing solution alternatives, and recommending NexusOne configurations that meet client performance, compliance, and AI readiness requirements. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [The Data Mesh as the end of the Datalake as we know it](https://www.wearedevelopers.com/videos/156-the-data-mesh-as-the-end-of-the-datalake-as-we-know-it) ## Related Articles - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)