Tech Lead - McKinsey Transformation

You will work in our McKinsey Client Capabilities Network in (EMEA) and will be part of our Wave Transformatics team.

Wave is a McKinsey SaaS product that equips clients to successfully manage improvement programs and transformations. Focused on business impact, Wave allows clients to track the impact of individual initiatives and understand how they affect longer term goals. It is a mix of an intuitive interface and McKinsey business expertise that gives clients a simple and insightful picture of what can otherwise be a complex process by allowing them to track the progress and performance of initiatives against business goals, budget and time frames.

Our Transformatics team builds data and AI products to provide analytics insights to clients and McKinsey teams involved in transformation programs across the globe. The current team is composed of data engineers, data scientists and Product Managers who are spread across several geographies. The team covers a variety of industries, functions, analytics methodologies and platforms – e.g. Cloud data engineering, advanced statistics, machine learning, predictive analytics, MLOps and generative AI.

You will collaborate closely with a team comprising data scientists, data engineers, product developers, and analytics-focused consultants.  

You will be expected to set the overall technical direction for all analytics and data products and coach the data engineers and data scientists to follow-up your direction and coach them where relevant. All this with the overall focus to bring advanced analytics capabilities into one of the firm's flagship products, named "Wave”. Your work will be the backbone for how McKinsey runs future Transformations, leveraging data science assets, to lead to our client's success.

In this role you will be responsible for collaborating with product management and your team to design and build true analytics products which are performant, robust and maintainable. You will set technical strategy and standards for our data-engineering and data-science teams on architecture, way-of-working, expected code quality and documentation, with the goal of building a long-term sustainable product and team. This includes extending tooling such as CI/CD pipelines, also considering new MLOps tooling such as feature stores or experiment tracking.

You will spend a significant portion of your time coaching more junior team members and helping them improve their mastery of core engineering principles mainly but also their domain knowledge.