Translate business requirements into robust & performant data pipelines with appropriate ETL/ELT and monitoring/logging steps for a wide variety of data sources, such as Databricks Unity Catalog, SAP HANA, Microsoft SSAS, Azure SQL Server, Oracle, Parquet, APIs, Excel & CSVs.
Lead and mentor others in advanced troubleshooting and root-cause analysis.
Independently perform and document requirements, business logic, impact assessment, and technical documentation to maintain and troubleshoot key systems and data assets.
Lead discussions with cross-divisional Stryker peers to collaboratively harness domain expertise and emerging capabilities to drive impact.
Provide helpful insights in discussions to identify opportunities in data architecture and data movement that enable business opportunities with key stakeholders.
Lead and deliver presentations and communications that build data engineering credibility and rapport to a medium-sized group, with some guidance.

Bachelor’s degree in computer science, data analytics, mathematics, statistics, data science, or related field, or 6 years of additional data engineering work experience in lieu of degree.
Minimum of 4 years’ experience in data engineering or related technical role.
Experience in building and managing end‑to‑end data engineering solutions, from problem definition and requirements through architecture, deployment, and maintenance.
Experience in SQL and proficiency in at least one programming language central to data engineering (e.g., Python, Powershell, R, Spark).
Experience with ETL/ELT processes, optimizing and maintaining data pipelines, and data warehouse architecture.
Experience with DataOps, DevOps, and infrastructure-as-code practices using GitHub/GitLab.
Experience using cloud‑based data platforms or distributed computing technologies.
Experience performing code reviews, enforcing quality standards, and deploying improvements to production environments.
Preferred Master’s degree.
Experience with Microsoft data services, including Power BI, Microsoft Fabric, Azure Data Factory, Azure SQL Server, and Databricks.
Experience with database administration.
Experience with a data-driven reporting platform, such as Power BI, Tableau, or SSRS.

Lead Data Engineer

Key skills