Senior Data Engineer I

McKinsey & Company Inc. US is seeking a Senior Data Engineer I in Columbus, OH. Independently lead / manage data engineering development projects in collaboration with cross-functional teams. Lead documentation efforts and solution delivery. Make informed decisions in optimal infrastructure option selection process, and lead, design, and plan production workstream/activities. Develop data engineering pipelines on cloud platforms for use cases in Corporate Banking domain. Lead deployment of data engineering solutions in production using: Python, PySpark, SQL, AWS, Azure. Create/manage data environment in cloud (internal/client) or on-premise. Own the relationship with mid-level clients and be able to move to action in area of expertise. Performs capability building for client data engineers/data scientists. Works closely with senior leadership on prioritization and delegation of tasks, cost, effort and time estimation for data engineering workstream on a project. Work without full time oversight from senior colleagues, and coach new joiners or oversee the work of junior colleagues. Identification, organization, and ingestion of data, spread across multiple sources (operational, analytical, and reporting). Work in collaboration with stakeholders and team members to analyze user needs and develop data integration solutions. Management of data including definition, usage, and quality via architecture repositories like data dictionaries, data models, metadata, and data quality logs. Design, build and support data pipelines for ingestion, transformation, conversion, and validation. Conduct data assessment, perform data quality checks, transform, and load raw data using SQL and ETL tools and it will contribute to the overall implementation of product solutions. Deploy statistical modeling and optimization techniques most suited for the business problem (using Python, SAS, SQL, R, and other relevant tools) to improve Risk management decision-making. Conduct hands-on rigorous quantitative analysis, including getting the data, cleaning it and exploring it for accuracy, and building statistical models using the data. Define data strategy and convert business requirements into advanced data architecture to derive actionable insights. Design, develop, and implement data solutions and products by building end-to-end data engineering pipelines. Contribute expertise in building and designing complex solution architecture to solve business problems and build data assets for business intelligence reporting and analytics. Technical lead for various data development initiatives which includes database as well as data engineering pipeline migrations. Build, own, and manage end-to-end data lifecycle which includes design, development and maintenance of reliable, scalable and maintainable data pipelines by applying data engineering design principles and best practices. Lead data reviews and contribute to designing data governance policies and frameworks to ensure best design practices are carried out across the data lifecycle. Provide technical mentorship, guidance and knowledge sharing sessions to various teams on data design standards and data architecture best practices. Work closely with management to develop standard reporting as well as develop data pipelines for complex analytical models. Learn about the landscape of data sources and tools that enable the client development and relationship-building efforts

Created: 2025-10-04