data engineer for transformation analytics

McKinsey & Company develops Wave, a SaaS product that helps clients manage improvement programs and transformations by tracking initiative progress, performance, budgets, timelines, and impact on longer-term goals. Its Transformatics team builds data and AI products that provide analytics insights for clients and McKinsey teams involved in transformation programs globally.

• Design, build, and optimize scalable data solutions for analytics, reporting, and machine learning;
• Develop robust data ingestion pipelines, procure data from APIs, and integrate it into cloud-based storage layers;
• Clean and standardize data to ensure data quality;
• Build next-generation cloud-based data platforms for rapid business data access and emerging technology incubation;
• Design and develop scalable, reusable data products for analytics, reporting, and machine learning pipelines;
• Implement query tuning, indexing, partitioning, and caching strategies in platforms such as Snowflake and Databricks;
• Collaborate with data scientists, engineers, and business teams to deliver analytics-ready datasets;
• Establish and enforce data governance practices aligned with SOC 2 and GDPR;
• Implement access controls, data lineage tracking, and encryption standards;
• Build resilient automated workflows using Step Functions and Databricks Workflows;
• Implement monitoring, logging, and alerting systems for reliability and data quality;
• Guide junior engineers and contribute to internal knowledge-sharing initiatives;
• Stay current with emerging technologies and champion continuous improvement in data engineering methodologies.

• 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 data products for analytics, automation, or machine learning;
• Deep expertise in AWS services, including S3, Lambda, Glue, and Snowflake;
• Experience designing scalable and cost-efficient data architectures;
• Proficiency in Python, including modularization and production-ready code for data transformations, automation, and workflow orchestration;
• Expert-level SQL skills, including query optimization, performance tuning, stored procedures, and database design;
• Experience designing and implementing scalable data pipelines with AWS Glue, Step Functions, and SQL-based transformations;
• Strong knowledge of data modeling, data warehousing, 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, Git, and automated deployment strategies;
• Strong problem-solving skills focused on troubleshooting and optimizing complex data workflows;
• Excellent communication and collaboration skills in agile, cross-functional teams;
• Ability to mentor junior engineers;
• Nice to have: Experience with Databricks, PySpark, and Delta Lake.

• Competitive salary based on location, experience, and skills;
• Comprehensive benefits package for employees and their families;
• Continuous learning, structured development programs, mentorship, coaching, and apprenticeship opportunities;
• Access to a global community of colleagues across 65+ countries and more than 100 nationalities.