Managing Consultant, Data Engineering

Introduction

At IBM, work is more than a job - it's a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you've never thought possible. Are you ready to lead in this new era of technology and solve some of the world's most challenging problems? If so, lets talk.

Your Role And Responsibilities

We are looking for a Managing Consultant, Data Engineering to join our growing team of experts. This position leads the design and delivery of complex data solutions across modern cloud platforms, with a strong emphasis on Snowflake. The work spans data architecture, data modeling, pipeline patterns, and defining architectural patterns that drive consistent, scalable data delivery across client engagements towards high-quality enterprise data and AI delivery outcomes.

The ideal candidate is as comfortable whiteboarding an architecture as they are rolling up their sleeves in delivery. They incorporate AI tooling into how they work and deliver, can advise clients on where AI fits into their data strategy, and know how to architect and build pipelines that move toward AI-ready outcomes - while keeping foundational data quality and structure intact.

This role carries team leadership responsibility across engagements of varying size and complexity. The Managing Consultant is accountable for delivery outcomes - keeping teams aligned, removing blockers, and ensuring the quality of what ships to the client. This person sets the technical direction for their teams, establishes standards, and mentors consultants at earlier stages of their career. They are equally capable of engaging at the client level - communicating architectural decisions, managing expectations, and building trust with stakeholders. This person thrives in a consulting environment where no two engagements are the same.

This role can be performed from anywhere in the US

Preferred Education

Master's Degree

Required Technical And Professional Expertise

• Bachelor's degree in Computer Science, Engineering, or equivalent field.
• 8 years in data engineering, architecture, or related technical roles.
• Deep expertise in data warehouse design and large-scale data modeling.
• Proven track record architecting high-throughput, real-time data ingestion frameworks.
• Hands-on experience with ETL/ELT tools such as Matillion or Informatica.
• Strong proficiency in SQL, Python, and Scala.
• Cloud platform experience across AWS, Azure, or GCP.
• Advanced Snowflake expertise, including performance optimization and platform-native features.
• Experience architecting data platforms that support AI and ML workloads.
• Familiarity with MLOps concepts and AI-powered pipeline development.
• Ability to advise clients on AI readiness and data maturity.
• Skilled at leveraging AI tools to improve team delivery outcomes.
• Proven ability to lead, mentor, and grow technical teams.
• Track record of managing delivery across complex, multi-workstream engagements.
• Strong client-facing communication and executive stakeholder engagement skills.
• Experience defining architectural standards and patterns across delivery teams.
• Ability to hire, develop, and performance-manage technical staff.
• Comfortable working in Agile delivery environments.
  
  

Preferred Technical And Professional Experience

• Advanced Snowflake Platform Knowledge: Experience with advanced Snowflake features, including data sharing, data pipelines, and data security. Ability to design and implement complex data and AI use cases on Snowflake, including familiarity with Snowflake Cortex and platform-native AI capabilities.
• Cloud Architecture Expertise: Experience designing scalable, secure cloud architectures for data and AI applications across AWS, Azure, or GCP. Knowledge of cloud migration, deployment, and management best practices at enterprise scale.
• Data Engineering Best Practices: Experience implementing data engineering best practices, including data modeling, data warehousing, and data governance. Ability to optimize data and AI solutions for performance and scalability across complex, multi-workstream engagements.
• AI & MLOps Familiarity: Exposure to MLOps practices, vector databases, feature stores, and LLM integration within data pipelines. Experience working at the intersection of data engineering and applied AI in production environments is a strong plus.
• AI Strategy & Client Advisory: Familiarity advising clients through AI readiness assessments and data maturity evaluations. Ability to define and govern AI tooling standards within a delivery team is highly valued.