Who you will work with
You will work in our McKinsey Client Capabilities Network in (EMEA) and will be part of our Wave Transformatics team.
Wave 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 provides analytics insights and products to consulting teams and clients involved in transformation programs across the globe. The team is composed of data engineers and data scientists who are spread across several geographies and who collaborate on a variety of industries, functions, analytics methodologies and platforms – e.g. Cloud data engineering, advanced statistics, machine learning, predictive analytics and generative AI. You will work closely with Transformatics and Wave leaders to help define the data strategy.
What you will do
You will manage a team of data engineers to architect, scale and maintain our transformation data platform, and to enable the development of new analytics offerings. You and your team will collaborate with software engineers, data scientists and analytics-focused consultants to integrate advanced analytics capabilities into the Wave product.
You will also support the development of knowledge for the firm’s transformation consultancy group and help influencing many of the recommendations our clients need to positively change their businesses and enhance performance of their transformation program.
Your key responsibilities will include:
- Acquiring, ingesting, and processing data from multiple sources and systems into centralized data marts
- Preparing data for analysis: profiling, cleaning, joining, transforming, enriching, aggregating, and filtering large and varied data sets
- Supporting data scientists: creating features, views, queries, datasets and data extracts through automation to help their analyses
- Adhering to Firm Information Security standards when requesting, extracting, ingesting and handling client data
- Creating reusable custom scripts, queries, and code commands for ad hoc data processing tasks
You will have the opportunity to gain new skills and build on the strengths you bring to the firm. In addition, you will be expected to coach and mentor other colleagues on data engineering topics, enabling them to grow and learn.
Your background
- Advanced degree in quantitative field like computer science, machine learning, applied statistics or mathematics; or equivalent experience
- 9+ years of relevant work experience
- Meaningful experience in building and maintaining large data sets to support data science development
- Mastery of Information Security principles to ensure compliant handling and management of client data
- Ability to work across structured, semi-structured, and unstructured data, extracting information and identifying linkages across disparate data sets
- Ability to understand complex systems and solve challenging analytical problems
- Ability to clearly communicate complex solutions to tech savvy and non tech savvy audiences
- Comfort with ambiguity and rapid changes common in early-stage product development
- Confirmed experience with the following technologies: AWS, Python, SQL, Tableau, GitHub
- Meaningful experience in Cloud platforms: Azure, Google Cloud; AWS is a must
- Knowledge about infrastructure deployment tools like Terraform and GitHub Actions is a plus
- Meaningful experience in ETL tools: Alteryx, MS SSIS, Talend, Pentaho, Domo; AWS Glue is a must
- Meaningful experience with on-premise and cloud-based data management/warehousing: MS SQL Server, Oracle, PostgreSQL; Snowflake is a must
- Meaningful experience in reporting and visualization tools: Power BI, MS SSRS; Tableau is a must
More Information
Application Details
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Organization Details
McKinsey
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