Job description

Work Flexibility: Hybrid As a Principal Architect, EDT Data Platform Engineering you will translate enterprise data architecture into scalable engineering solutions on the Azure and Databricks Lakehouse platform. You will partner with delivery teams, business representatives, and data stewards to build governed data pipelines, analytics-ready models, and reusable automation that improves consistency across projects and teams. What you will do: Technical Responsibilities: Build and maintain ingestion frameworks using Azure Data Factory, Databricks, and Spark. Implement Bronze-to-Silver transformations with data quality checks, schema validation, and data contract rules. Optimize batch and streaming pipelines for performance and cost. Own CI/CD pipelines in Azure DevOps and automate project onboarding, including repositories, pipelines, policies, and templates. Maintain Infrastructure as Code and automation using Terraform, PowerShell, and Python; troubleshoot pipeline failures and permission issues. Design curated Gold-layer data models, including star and snowflake schemas, fact tables, dimensions, surrogate keys, and slowly changing dimensions. Align reusable models, KPI definitions, and business logic with the enterprise semantic layer in Power BI, Microsoft Fabric, and downstream tools. Manage model changes through impact analysis, backward compatibility, regression support, and documented column derivation logic. Implement Unity Catalog standards, naming conventions, metadata policies, data quality frameworks, and role- and attribute-based access controls. Translate high-level architecture into low-level designs, reusable templates, and engineering playbooks. Leadership & People Management Responsibilities: Guide delivery teams through onboarding, debugging, and design reviews. Create reusable assets and hands-on guidance that build engineering capability and reduce repeat questions. Knowledge and Capabilities: Assess complex data systems, identify patterns, and recommend practical solutions. Explain technical concepts clearly to technical and non-technical stakeholders. Partner with business representatives and data stewards to define requirements, validate metrics and edge cases, and guide solution decisions. Identify opportunities for process optimization, redesign, and new or updated policies. What You Need: Required Qualifications Bachelor s degree from an accredited university in Computer Science, Engineering, Information Systems, Business, or an equivalent field of study. Minimum 8 years of experience in data engineering, data warehousing, and data and analytics platforms, including recent hands-on delivery on Azure and Databricks Lakehouse platforms. Experience with Azure Data Factory, Azure Data Lake Storage, Azure SQL, Databricks Delta Live Tables, Delta Lake, Spark, Workflows, and Unity Catalog. Advanced experience with PySpark or Python and SQL, including batch and streaming pipeline design. Experience with Azure DevOps pipelines, repositories, and policies; PowerShell automation; CI/CD; and Infrastructure as Code. Experience with schema enforcement, data contracts, data quality frameworks, metadata-driven design, monitoring, debugging, and performance tuning. Preferred Qualifications Master s degree in Management Information Systems, Business Administration, or a related discipline, or an Azure analytics certification. Experience with Microsoft Fabric Experience within the medical device or another regulated industry. United States of America Pay Ranges: USN : $135,600 - $225,900 USD Annual US10 : $149,200 - $248,500 USD Annuall US20 : $162,700 - $271,100 USD Annual View the U.S. work location and transparency guide to find the pay range for your location. Travel Percentage: 20% Stryker Corporation is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, gender identity, sexual orientation, national origin, disability, or protected veteran status. Stryker is an EO employer – M/F/Veteran/Disability. Stryker Corporation will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information.

Requirements

Must-haves

Work Flexibility: Hybrid
Build and maintain ingestion frameworks using Azure Data Factory, Databricks, and Spark.
Implement Bronze-to-Silver transformations with data quality checks, schema validation, and data contract rules.
Optimize batch and streaming pipelines for performance and cost.
Own CI/CD pipelines in Azure DevOps and automate project onboarding, including repositories, pipelines, policies, and templates.
Maintain Infrastructure as Code and automation using Terraform, PowerShell, and Python; troubleshoot pipeline failures and permission issues.
Design curated Gold-layer data models, including star and snowflake schemas, fact tables, dimensions, surrogate keys, and slowly changing dimensions.
Align reusable models, KPI definitions, and business logic with the enterprise semantic layer in Power BI, Microsoft Fabric, and downstream tools.
Manage model changes through impact analysis, backward compatibility, regression support, and documented column derivation logic.
Implement Unity Catalog standards, naming conventions, metadata policies, data quality frameworks, and role- and attribute-based access controls.
Translate high-level architecture into low-level designs, reusable templates, and engineering playbooks.
Guide delivery teams through onboarding, debugging, and design reviews.
Create reusable assets and hands-on guidance that build engineering capability and reduce repeat questions.
Assess complex data systems, identify patterns, and recommend practical solutions.
Explain technical concepts clearly to technical and non-technical stakeholders.
Partner with business representatives and data stewards to define requirements, validate metrics and edge cases, and guide solution decisions.
Identify opportunities for process optimization, redesign, and new or updated policies.
Bachelor s degree from an accredited university in Computer Science, Engineering, Information Systems, Business, or an equivalent field of study.
Minimum 8 years of experience in data engineering, data warehousing, and data and analytics platforms, including recent hands-on delivery on Azure and Databricks Lakehouse platforms.
Experience with Azure Data Factory, Azure Data Lake Storage, Azure SQL, Databricks Delta Live Tables, Delta Lake, Spark, Workflows, and Unity Catalog.
Advanced experience with PySpark or Python and SQL, including batch and streaming pipeline design.

Nice-to-haves

Master s degree in Management Information Systems, Business Administration, or a related discipline, or an Azure analytics certification.
Experience with Microsoft Fabric
Experience within the medical device or another regulated industry.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

azureci/cdpythonsql

Platform: workday
Effort: long