You will work in our McKinsey Client Capabilities Network in EMEA and will be part of our Wave Transformatics team, based in the Brussels office.

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 project 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.

As a member of the team, you will be responsible for designing, building, and optimizing scalable data solutions that power analytics, reporting, and machine learning. Working alongside a team of data engineers across global hubs, you will lead the development of robust data ingestion pipelines, procuring data from APIs and integrating it into cloud-based storage layers while ensuring data quality through rigorous cleaning and standardization.

In this role, you will play a key part in building next-generation cloud-based data platforms that enable rapid data access for business stakeholders and support the incubation of emerging technologies. Your work will focus on designing and developing scalable, reusable data products that serve as the foundation for analytics, reporting, and machine learning pipelines.

You will lead the architecture and optimization of data pipelines, driving the design and enhancement of ETL/ELT workflows using tools such as AWS Lambda, AWS Glue, Snowflake, and Databricks. Ensuring high performance, scalability, and cost efficiency in data processing solutions will be a critical aspect of your responsibilities. Managing and scaling cloud-based data infrastructure will also be central to your role, including configuring optimized storage solutions like S3, Snowflake, and Delta Lake, and overseeing computation resources to support efficient data operations.

Your expertise will be instrumental in implementing advanced performance optimization techniques, such as query tuning, indexing strategies, partitioning, and caching, to maximize efficiency in platforms like Snowflake and Databricks. Collaboration will be a key focus, as you work closely with data scientists, engineers, and business teams to deliver well-structured, analytics-ready datasets that drive insights and power machine learning initiatives.

In addition to technical responsibilities, you will establish and enforce data governance practices, ensuring compliance with industry standards such as SOC 2 and GDPR. This includes implementing robust access controls, tracking data lineage, and maintaining encryption standards to safeguard data security. Automation and monitoring will be integral to your work, as you build resilient, automated workflows using tools like Step Functions and Databricks Workflows, while also implementing proactive monitoring, logging, and alerting systems to ensure reliability and data quality.

Mentorship and innovation will be key aspects of your role, as you guide junior engineers, contribute to internal knowledge-sharing initiatives, and stay ahead of emerging technologies. By championing continuous improvement in data engineering methodologies, you will help drive innovation and elevate the organization’s data capabilities to new heights.

• Continuous learning: Our learning and apprenticeship culture, backed by structured programs, is all about helping you grow while creating an environment where feedback is clear, actionable, and focused on your development. The real magic happens when you take the input from others to heart and embrace the fast-paced learning experience, owning your journey.
• A voice that matters: From day one, we value your ideas and contributions. You’ll make a tangible impact by offering innovative ideas and practical solutions. We not only encourage diverse perspectives, but they are critical in driving us toward the best possible outcomes.
• Global community: With colleagues across 65+ countries and over 100 different nationalities, our firm’s diversity fuels creativity and helps us come up with the best solutions for our clients. Plus, you’ll have the opportunity to learn from exceptional colleagues with diverse backgrounds and experiences.
• World-class benefits: On top of a competitive salary (based on your location, experience, and skills), we provide a comprehensive benefits package to enable holistic well-being for you and your family.

Bachelor’s or master’s degree in computer science, Engineering, or a related technical field

5+ years of hands-on experience in data engineering, ETL/ELT development, cloud-based data solutions, or building data products that serve analytics, automation, or machine learning needs

Deep expertise in AWS cloud services, including S3, Lambda, Glue, Snowflake, and designing scalable, cost-efficient data architectures

Proficiency in Python, including modularization, writing optimized, production-ready code for data transformations, automation, and workflow orchestration

Expert-level SQL skills, including query optimization, performance tuning, stored procedures, and database design best practices

Proven experience in designing and implementing scalable data pipelines using AWS Glue, Step Functions, and SQL-based transformations (using stored procedures)

Strong knowledge of data modeling, data warehousing concepts, schema design, and partitioning strategies

Hands-on experience with Tableau or other BI tools for data visualization and dashboard development

Hands-on experience with DevOps and CI/CD, including infrastructure-as-code, version control (Git) and automated deployment strategies

Strong problem-solving skills, with a focus on troubleshooting and optimizing complex data workflows for performance, reliability, and scalability

Experience with Databricks for scalable data processing, PySpark for distributed data transformations, and Delta Lake for data versioning (Preferred)

Excellent communication and collaboration skills, with the ability to work effectively in agile, cross-functional teams and mentor junior engineers