> Markdown version of [/jobs/ext/1928296-senior-data-engineer-ai-analytics-infrastructure](https://www.wearedevelopers.com/jobs/ext/1928296-senior-data-engineer-ai-analytics-infrastructure). 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). --- # Senior Data Engineer - AI & Analytics Infrastructure - **Company:** IBM - **Location:** New York, NY, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Query Performance, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Automation of Tests, Microsoft Azure, Continuous Integration, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Transformation, Document-Oriented Databases, Meta-Data Management, Cloud Services, Azure Data Lake, Systems Integration, Enterprise Data Management, Azure Service Bus, Enterprise Software Applications, Data Storage Technologies, Feature Engineering, Azure Data Factory, Snowflake, Event Driven Architecture, Microsoft Fabric, Information Technology, Data Lineage, AWS Glue, Data Analytics, Machine Learning Operations, Virtual Agents, Azure Synapse Analytics, Data Pipelines, Databricks - **Published:** August 5, 2026 - **Apply:** https://dejobs.org/x/x/DC618783761B40C2841B95DC419B0E4A/job/ ## About the Role Preferred technical and professional experience Preferred Skills * Familiarity with Azure Data Factory, Event Hubs, or other Azure data integration services * Experience implementing data governance frameworks and working with data cataloging tools * Knowledge of MLOps data pipelines and feature engineering for AI model consumption * Background supporting Agentic AI or generative AI programs where data quality is mission-critical ## Description We are seeking an experienced Data Engineer to support the design and scaling of data pipelines and infrastructure for a high-priority Agentic AI engagement. This role is central to the success of the program - the quality, accessibility, and governance of data directly enables the AI and analytics use cases being built. You will work alongside AI architects and engineers to ensure that the right data reaches the right systems in the right form. The client is looking for someone with strong hands-on experience across modern data platforms who can operate with confidence and deliver at pace. What You'll Do Data Pipeline Design & Development * Design, build, and maintain robust data pipelines that ingest, transform, and deliver high-quality data across the platform * Develop scalable architectures using Microsoft Fabric, Databricks, and/or Azure Synapse Analytics * Ensure pipelines are performant, reliable, and built to handle the scale and variability of enterprise data * Implement data transformation and orchestration workflows that feed AI models and analytics dashboards Data Infrastructure & Architecture * Architect and maintain the underlying data infrastructure that supports AI and analytics use cases * Define and implement data lakehouse patterns, medallion architecture, and layered data models * Collaborate with AI engineers and architects to ensure data outputs are structured and accessible for model consumption * Manage and optimize data storage, compute, and processing environments for cost and performance Data Quality & Governance * Implement data quality checks, validation frameworks, and monitoring to ensure trustworthy data outputs * Establish and enforce data governance standards including lineage tracking, cataloging, and access controls * Partner with stakeholders to document data assets and ensure discoverability across the platform, * 7+ years of experience designing, developing, and maintaining scalable batch and real-time data pipelines across Azure and AWS. * Build and optimize enterprise data platforms leveraging services such as Azure Data Factory, Azure Data Lake, AWS S3, AWS Glue, Databricks, and Snowflake. * Develop robust ETL/ELT frameworks supporting analytics, reporting, operational, and AI/ML use cases across cloud and hybrid ecosystems. * Implement scalable ingestion and transformation pipelines for structured, semi-structured, and unstructured enterprise data sources. * Support data industrialization efforts through reusable pipeline frameworks, standardized engineering practices, observability, monitoring, automated testing, and CI/CD deployment patterns. * Enable trusted enterprise data foundations by implementing data quality controls, metadata management, lineage, cataloging, and governance capabilities. * Optimize data models, distributed processing workloads, storage strategies, and query performance within Databricks and Snowflake environments. * Integrate enterprise applications, APIs, ERP systems, CRM platforms, and event-driven architectures into centralized cloud data platforms. * Collaborate with AI engineers, architects, analysts, and business stakeholders to support analytics, AI, and generative AI initiatives. * Support Infrastructure-as-Code, cloud-native deployment practices, and secure enterprise data operations across Azure and AWS platforms. ## Related Videos - [How we built an AI-powered code reviewer in 80 hours](https://www.wearedevelopers.com/videos/1511-how-we-built-an-ai-powered-code-reviewer-in-80-hours) - [WeAreDevelopers LIVE - CSS is DOOMed](https://www.wearedevelopers.com/videos/1838-wearedevelopers-live-css-is-doomed) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Tips, Techniques, and Common Pitfalls Debugging Kafka](https://www.wearedevelopers.com/videos/838-tips-techniques-and-common-pitfalls-debugging-kafka) - [Data Fabric in Action - How to enhance a Stock Trading App with ML and Data Virtualization](https://www.wearedevelopers.com/videos/253-data-fabric-in-action-how-to-enhance-a-stock-trading-app-with-ml-and-data-virtualization) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [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) - [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)