Managing Consultant, Data Engineering

IBM is a leading technology company that is seeking a Managing Consultant in Data Engineering to join their expert team. This role involves leading the design and delivery of complex data solutions, focusing on data architecture and AI integration across cloud platforms.

Responsibilities:

• Lead the design and delivery of complex data solutions across modern cloud platforms, with a strong emphasis on Snowflake
• Incorporate AI tooling into work and delivery, advising clients on AI's role in their data strategy
• Architect and build pipelines aimed at AI-ready outcomes while maintaining data quality
• Carry team leadership responsibility across engagements of varying size and complexity
• Accountable for delivery outcomes, keeping teams aligned, removing blockers, and ensuring quality
• Set the technical direction for teams, establish standards, and mentor early-career consultants
• Engage at the client level, communicating architectural decisions and managing expectations
• Build trust with stakeholders in a consulting environment

Requirements:

• 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
• 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