Senior Data Engineer - AI & Analytics Infrastructure
Not Disclosed•Full-TimeOn-site
location_on4330, Dickason Avenue, Dallas, Dallas County, Texas, 75219, United States
About IBM Consulting
A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You'll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you'll have the tools to drive meaningful change and accelerate client impact.
At IBM Consulting, curiosity fuels success. You'll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.
About the Role
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, as the quality, accessibility, and governance of data directly enable 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. Your work will ensure that data pipelines are performant, reliable, and built to handle the scale and variability of enterprise data, while implementing the data quality checks and governance standards necessary for trustworthy AI outputs.
Work location
Work model: On-site
4330, Dickason Avenue, Dallas, Dallas County, Texas, 75219, United States
Dallas, Texas
Key Responsibilities
- check_circleDevelop scalable architectures using Microsoft Fabric, Databricks, and Azure Synapse Analytics
- check_circleArchitect and maintain underlying data infrastructure supporting AI and analytics use cases
- check_circleDefine and implement data lakehouse patterns, medallion architecture, and layered data models
- check_circleImplement data quality checks, validation frameworks, and monitoring for trustworthy outputs
- check_circleEstablish and enforce data governance standards including lineage tracking and access controls
- check_circleOptimize data storage, compute, and processing environments for cost and performance
- check_circleCollaborate with AI engineers to ensure data outputs are structured for model consumption
- check_circleDesign, build, and maintain robust data pipelines for ingesting and transforming enterprise data
Requirements
- verified7+ years of experience designing, developing, and maintaining scalable batch and real-time data pipelines across Azure and AWS
- verifiedExperience building and optimizing enterprise data platforms leveraging Azure Data Factory, Azure Data Lake, AWS S3, AWS Glue, Databricks, and Snowflake
- verifiedExperience developing robust ETL/ELT frameworks supporting analytics, reporting, operational, and AI/ML use cases
- verifiedExperience implementing scalable ingestion and transformation pipelines for structured, semi-structured, and unstructured enterprise data sources
- verifiedExperience supporting data industrialization efforts through reusable pipeline frameworks, standardized engineering practices, observability, monitoring, automated testing, and CI/CD deployment patterns
- verifiedExperience enabling trusted enterprise data foundations by implementing data quality controls, metadata management, lineage, cataloging, and governance capabilities
Nice to Have
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.
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Frequently asked questions about Senior Data Engineer - AI & Analytics Infrastructure at IBM
What does a Senior Data Engineer - AI & Analytics Infrastructure at IBM do?expand_more
In this Senior Data Engineer - AI & Analytics Infrastructure at IBM role, you will develop scalable architectures using microsoft fabric, databricks, and azure synapse analytics; architect and maintain underlying data infrastructure supporting ai and analytics use cases; define and implement data lakehouse patterns, medallion architecture, and layered data models; and implement data quality checks, validation frameworks, and monitoring for trustworthy outputs.
What are the requirements for this Senior Data Engineer - AI & Analytics Infrastructure role?expand_more
IBM is looking for candidates who meet the following requirements: 7+ years of experience designing, developing, and maintaining scalable batch and real-time data pipelines across Azure and AWS; Experience building and optimizing enterprise data platforms leveraging Azure Data Factory, Azure Data Lake, AWS S3, AWS Glue, Databricks, and Snowflake; Experience developing robust ETL/ELT frameworks supporting analytics, reporting, operational, and AI/ML use cases; Experience implementing scalable ingestion and transformation pipelines for structured, semi-structured, and unstructured enterprise data sources; Experience supporting data industrialization efforts through reusable pipeline frameworks, standardized engineering practices, observability, monitoring, automated testing, and CI/CD deployment patterns; and Experience enabling trusted enterprise data foundations by implementing data quality controls, metadata management, lineage, cataloging, and governance capabilities.
Where is the Senior Data Engineer - AI & Analytics Infrastructure role at IBM located?expand_more
Senior Data Engineer - AI & Analytics Infrastructure at IBM is based in 4330, Dickason Avenue, Dallas, Dallas County, Texas, 75219, United States. This is a on-site role.
Is this Senior Data Engineer - AI & Analytics Infrastructure job remote, hybrid, or on-site?expand_more
IBM has listed this Senior Data Engineer - AI & Analytics Infrastructure role as on-site.
How much experience is required for this Senior Data Engineer - AI & Analytics Infrastructure role?expand_more
Senior Data Engineer - AI & Analytics Infrastructure at IBM typically requires 7+ years of relevant experience at the senior level.
What skills do you need for the Senior Data Engineer - AI & Analytics Infrastructure role at IBM?expand_more
Key skills for Senior Data Engineer - AI & Analytics Infrastructure at IBM include Azure; Aws; Azure Data Factory; Azure Data Lake; Aws S3; Aws Glue; Databricks; and Snowflake.
What category does the Senior Data Engineer - AI & Analytics Infrastructure role belong to?expand_more
Senior Data Engineer - AI & Analytics Infrastructure at IBM is part of the it job category on Recrutus.