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.
Задачи
- 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
Требования
- 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
Условия
- 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
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data engineer for transformation analytics in London employer: Enfint
AJ Bell is an exceptional employer that fosters a collaborative and innovative work culture in the heart of London. With a strong emphasis on employee growth, you will have the opportunity to mentor junior designers while working on impactful design systems that prioritise accessibility and usability. Enjoy a supportive environment that values proactive communication and offers unique benefits tailored to enhance your professional journey.
