Senior Data Platform Leader: AI & ML Pipelines Architect

McKinsey; Boston (MA); On-site; USD 217,000 - 220,000; Full time

Job summary

McKinsey is seeking a senior Data Engineering leader to drive the design and delivery of enterprise data and AI platforms across North America. You will guide multiple technical teams, architect scalable pipelines, and mentor junior engineers while collaborating with clients at all levels.

You will partner with QuantumBlack teams on cutting-edge AI projects, contribute to R&D, and help scale ML systems in production, ensuring governance and security standards are upheld.

Qualifications

• 10+ years relevant professional experience
• Ability to scope projects, define workstreams, and translate roadmaps into sprint goals, milestones, and deliverables within agile and iterative team environments
• Proven experience architecting and building enterprise data and AI platforms as well as scalable data pipelines for production analytics
• Passion for and track record of effectively leading and mentoring more junior colleagues
• Expertise in implementing software engineering best practices to drive enterprise-wide improvement, including DevOps, DataOps, and MLOps
• Strong software engineering and data engineering expertise, including distributed computing frameworks (e.g., Spark, Dask), cloud platforms (AWS, Azure, GCP), containerization (Docker, Kubernetes), analytics libraries (e.g., pandas, NumPy, matplotlib), and modern data platforms and orchestration tools such as Databricks, Airflow, SQL, PySpark, Kedro, and RAPIDS
• Ability to write clean, maintainable, scalable, and robust code in object-oriented languages such as Python, Scala, or Java, and experience supporting the development of junior engineers through collaborative code reviews
• Extensive commercial client-facing or senior stakeholder management experience
• Exceptional time management to meet your responsibilities in a complex and largely autonomous work environment
• Willingness to travel
• Strong communication skills with the ability to adjust your style to suit different perspectives and seniority levels

Responsibilities

• Lead technical teams, architect data platforms, and build scalable pipelines while mentoring colleagues and collaborating with clients at all levels.
• Tackle impactful challenges, contribute to R&D, and grow as a leader by working with diverse experts on cutting-edge AI projects across industries.
• Lead multiple technical teams and own the technology stack for enterprise data and AI platforms; design and build robust, scalable data pipelines for ML while ensuring information security standards are upheld.
• Mentor junior colleagues, contribute to R&D projects, and drive innovation within the QuantumBlack, AI by McKinsey Data Engineering community.
• Partner with QuantumBlack, AI by McKinsey and QuantumBlack Labs teams to create innovative ML systems that accelerate AI adoption and solve business problems at speed and scale.
• Work in North America offices as part of our global Data Engineering community; collaborate in cross-functional Agile teams with Data Scientists, ML Engineers, and industry experts to deliver advanced analytics solutions.
• Collaborate with clients from data owners to C-level executives to architect impactful solutions that solve complex business problems and drive value.

Skills

Technical leadership

Data engineering

AI platforms

DevOps/MLOps

Spark/Dask

Cloud platforms

Docker/Kubernetes

Python/Scala/Java

Stakeholder management

Communication skills

Education

Degree in Computer Science, Engineering, or Mathematics

Tools

Databricks

Airflow

SQL

PySpark

Kedro

RAPIDS

Job description

McKinsey is seeking a senior Data Engineering leader to drive the design and delivery of enterprise data and AI platforms across North America. You will guide multiple technical teams, architect scalable pipelines, and mentor junior engineers while collaborating with clients at all levels.

You will partner with QuantumBlack teams on cutting-edge AI projects, contribute to R&D, and help scale ML systems in production, ensuring governance and security standards are upheld.