Data Modeling and Warehouse Engineer

The Professional, Data Modeling and Warehouse Engineer designs, builds, and optimizes the data models, data warehouses, and integration pipelines that form the analytical backbone of DePuy Synthes. Sitting within the Data & AI organization, this role is responsible for translating business and data requirements into robust, scalable, engineering-oriented data warehouse solutionsdeveloping conceptual, logical, and physical data models and enabling accurate, timely, and accessible data across the enterprise. As an established individual contributor working with minimal supervision, the engineer partners with data architects, data engineers, data scientists, and business stakeholders to reduce redundancy, enhance data quality, and optimize overall data/information flow within security boundaries. The role directly supports the organization's overall Data Analytics & Computational Sciences strategy and the build-out of enterprise data capabilities.

• Design, develop, and maintain conceptual, logical, and physical data models that support enterprise reporting, analytics, and machine learning use cases.
• Build and enhance the data warehouse, including data marts, dimensional models (star/snowflake schemas), and semantic layers optimized for performance and scalability.
• Develop and optimize ETL/ELT pipelines to ingest, transform, cleanse, and integrate structured and unstructured data from multiple source systems.
• Contribute to the design, analysis, and implementation of architectural models and data integration for a sound, engineering-oriented data warehouse.
• Establish and apply data modeling standards, naming conventions, and governance practices to ensure consistency across upstream and downstream data channels.
• Optimize data storage, partitioning, indexing, and query performance to improve overall data/information flow and reduce redundancy.
• Monitor and resolve data integration failures to correct and maximize data repository performance and reliability.
• Define data repository requirements, data dictionaries, and metadata to support accessibility, lineage, and reusability.
• Collaborate with data architects to refactor and evolve next-generation information architecture designs.
• Ensure data integrity, security, and compliance throughout the data lifecycle, adhering to data governance and privacy standards.
• Partner with cross-functional teams to translate business requirements into technical data solutions and reusable data assets.
• Support data quality initiatives by profiling data, identifying anomalies, and implementing cleansing and validation rules.
• Prepare and maintain technical documentation, including data flow diagrams, model specifications, and design decisions.

What you'll bring

Education:

• Required: Bachelor's degree in Computer Science, Information Systems, Data Engineering, Engineering, or a related quantitative/technical field.
• Preferred: Master's degree in Computer Science, Data Engineering, Information Management, or a related discipline.

Experience and Skills

Required

• 3+ years of experience in data modeling, data warehousing, or data engineering.
• Strong expertise in data modeling techniques (dimensional modeling, 3NF, star/snowflake schemas) and modeling tools (e.g., Erwin, ER/Studio, dbt).
• Advanced SQL skills, including performance tuning and query optimization.
• Hands-on experience building and maintaining data warehouses and ETL/ELT pipelines (e.g., Informatica, Talend, dbt, Airflow).
• Experience with cloud data warehouse platforms (Databricks)
• Proficiency with a programming/scripting language (e.g., Python, Scala) for data processing and automation.
• Solid understanding of data governance, metadata management, and data quality practices.

Preferred

• Experience in a regulated industry (MedTech, Pharmaceutical, or Healthcare) with familiarity in associated data governance and compliance requirements.
• Experience with big data and lakehouse ecosystems and distributed processing (e.g. Databricks).
• Familiarity with data cataloging, lineage, and master data management tools (e.g., Databricks Unity Catalog).
• Exposure to BI/visualization platforms (e.g., Tableau, Power BI) and the data structures that support them.
• Experience working in Agile environments and cross-functional product teams.