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 work alongside a team of skilled data engineers to design, build, and optimize scalable data solutions that power analytics, reporting, and machine learning. As a data engineer, you will be responsible for procuring data from APIs, ingesting it into the data storage layer, and ensuring its quality through cleaning and standardization. You will develop scalable data ingestion pipelines that integrate on cloud ecosystems, making data readily available for analytics and reporting.

You will also be part of an effort to build next-gen data platforms on the cloud to enable our business stakeholders to have rapid data access and incubate emerging technologies. You will be part of the team managing data governance, ensuring necessary data security and lifecycle controls are in place.

• 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, all while upholding our unwavering commitment to ethics and integrity. 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 discipline

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

Strong foundational knowledge of AWS cloud services, including S3, Lambda, Glue, and Snowflake, with a focus on scalable and cost-efficient data architectures

Proficiency in Python, with experience in modularization, writing optimized, production-ready code for data transformations and automation

Advanced SQL skills, including query optimization, performance tuning, and database design

Experience in building robust and scalable data pipelines using AWS Glue, Step Functions and SQL-based transformations (using stored procedures)

Solid understanding of data modeling, data warehousing concepts, and schema design best practices

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

Exposure to DevOps and CI/CD practices, including infrastructure-as-code, version control (Git), and automated deployment strategies

Strong problem-solving mindset, with the ability to troubleshoot and optimize complex data workflows efficiently

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

Experience with Databricks for scalable data processing and PySpark for distributed data transformations (preferred)