Your Growth
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.
Your Impact
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.
In this role, you will design and build data products. You will develop and maintain scalable, reusable data products that serve as foundational assets for analytics, reporting, and machine learning pipelines.
You will build and maintain data pipelines. You will design, develop, and optimize ETL/ELT workflows using tools like AWS Lambda, AWS Glue, Snowflake, or Databricks to ensure efficient and scalable data processing.
You will manage data infrastructure. You will work with cloud platforms like AWS to configure and manage storage (S3), compute resources, and workflow orchestration.
You will optimize performance. You will improve query efficiency, indexing strategies, and partitioning techniques to enhance data processing speed and cost-effectiveness in Snowflake, or Delta Lake (Databricks).
You will work closely with data scientists, product owners, and business stakeholders to provide clean, structured datasets that enable advanced analytics and machine learning.
You will ensure data governance and security. You will implement best practices for access control, data lineage tracking, and regulatory compliance (SOC 2, GDPR).
You will automate and monitore workflows. You will be responsible to develop scalable data pipeline automation using orchestration tools like Step Functions, or Databricks Workflows. Implement logging, alerting, and monitoring solutions to ensure data quality, reliability, and system performance.
You will stay updated on new technologies, participate in hackathons, and contribute to improving data engineering best practices across the team.
You will work in our McKinsey Client Capabilities Network in EMEA and will be part of our Wave Transformatics team.
Your qualifications and skills
- 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)
