Data engineer for transformation analytics

Описание

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 Dev Ops 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