Senior Data Engineer - AI & Analytics Infrastructure

Introduction

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.

Your Role And Responsibilities

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
  
  

Preferred Education

Master's Degree

Required Technical And Professional Expertise

• 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.
  
  

Preferred Skills

Preferred technical and professional experience

• 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