> Markdown version of [/jobs/ext/1743714-sr-ai-data-migration-engineer-onsite](https://www.wearedevelopers.com/jobs/ext/1743714-sr-ai-data-migration-engineer-onsite). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # SR AI Data Migration Engineer- Onsite - **Company:** Cognizant Technology Solutions Corporation - **Location:** Washington, DC, United States - **Experience:** Expert - **Salary:** $125,000.0 - $140,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Information Engineering, Data Migration, Entity Relationship Models, Graph Database, Python (Programming Language), Azure Machine Learning, SQL Databases, Data Streaming, Apache Spark, Information Technology, Real Time Data, Machine Learning Operations, Cloudwatch, Amazon Simple Queue Service (SQS), Data Pipelines, Dynatrace, Databricks, Microservices - **Published:** July 3, 2026 - **Apply:** https://dejobs.org/x/x/191E3649901847DCA02FB9C99D41F73A/job/ ## About the Role 3.1 Education · Bachelor's or Master's in Computer Science, Data Engineering, or a related quantitative field. 3.2 Certifications (Preferred) · AWS Certified Data Engineer - Associate or Microsoft Certified Azure Data Engineer. OR AWS ML Specialty 3.3 Mandatory Experience · 5+ years building continuous data pipelines, real-time streaming architectures, and preparing data for machine learning workflows. 3.4 Technical Knowledge · Expert SQL and Python. · Deep expertise in AWS ecosystem (EKS, Lambda, SQS, OpenSearch, Neptune, Bedrock) and Apache Spark/Databricks. 3.5 Core Competencies · Strong architectural mindset, capability to handle hight-veolocity data, and enthusiasm for integrating foundational AI/ML services. ## Description The Data Engineer in this role will support programs involving one or more of the following: responsible for building persistent cloud data pipelines, integrating microservices, and structuring data to power Amazon OpenSearch, Amazon Neptune (Knowledge Graphs), and Amazon SageMaker for advanced analytics. 2. Scope of Work 2.1 Pipeline Development and Implementation · Build continuous, event-driven streaming pipelines using Amazon EventBridge and SQS. · Orchestrate complex ELT transformations using EKS and Databricks. · Develop automated data feeds for the enterprise data platform and downstream applications. 2.2 Solution Design and Optimization · Design and populate graph data models for Amazon Neptune to support entity relationship tracking. · Build and optimize vector indexes for Amazon OpenSearch to power the platform's AI/ML and RAG Q&A capabilities. · Ensure real-time or near-real-time data latency targets are met for operational dashboards. 2.3 Stakeholder Engagement and Change Management · Partner directly with AI/ML Data Scientists to ensure data is properly curated, partitioned, and served for real-time model inference. · Support front-end developers by building reliable, performant data APIs. · Present pipeline architectures during technical reviews. 2.4 Governance, Ethics, and Risk · Implement fine-grained, row-level Access Control to secure sensitive procurement data. · Set up Amazon CloudWatch and Dynatrace for continuous pipeline monitoring, alerting, and telemetry. · Ensure data served to AI models is clean and unbiased according to institutional guidelines. 2.5 Documentation and Reporting · Maintain architectural diagrams for streaming data flows. · Write API endpoint documentation and AI data prep runbooks. ## Related Videos - [Reference Architecture of AI in the Cloud](https://www.wearedevelopers.com/videos/1613-reference-architecture-of-ai-in-the-cloud) - [From Black Box to Glass Box : Bedrock AgentCore Observability](https://www.wearedevelopers.com/videos/2126-from-black-box-to-glass-box-bedrock-agentcore-observability) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [The Power of Purpose: Unlocking Potential and Innovation](https://www.wearedevelopers.com/videos/1110-the-power-of-purpose-unlocking-potential-and-innovation) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [30 powerful AWS hacks in just 30 minutes: Boost your developer productivity](https://www.wearedevelopers.com/videos/1624-30-powerful-aws-hacks-in-just-30-minutes-boost-your-developer-productivity) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [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) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)