Senior Data Engineer (Remote)

Stryker is seeking a Senior Data Engineer to join its Customer Intelligence Data Engineering team. The role focuses on delivering data engineering projects, gathering requirements, building ETL pipelines, documenting data assets, and supporting scalable data architecture and integration solutions.

Responsibilities:

• Translate business and stakeholder needs into scalable data engineering and data science solutions
• Partner with cross-functional teams to understand requirements, prioritize opportunities, and deliver data-driven outcomes
• Communicate technical concepts, project updates, and analytical findings to business and technical stakeholders through presentations, reports, and visualizations
• Apply data engineering best practices to solve complex business problems and support decision-making
• Gather, document, and refine business and technical requirements for data products, platforms, and solutions
• Create and maintain technical documentation for data assets, pipelines, systems, and processes
• Support the design and implementation of data architecture, data movement, and integration solutions that enable business growth and operational efficiency
• Contribute to continuous improvement initiatives focused on data quality, performance, scalability, and automation

Requirements:

• Bachelor's degree in Computer Science, Data Engineering, Data Analytics, Mathematics, Statistics, Data Science, Information Systems, or a related field
• Minimum 2 years of professional experience in data engineering, data architecture, analytics engineering, or other data related disciplines
• Experience developing and maintaining data pipelines, data integrations, and data solutions in cloud-based environments
• Proficiency in at least one core data engineering language such as Python, SQL, or Spark
• Experience working with object-oriented programming concepts and data structures
• Knowledge of ETL/ELT development, data modeling, data warehousing, and distributed data processing
• Master's degree or PhD in Computer Science, Data Science, Engineering, Statistics, Mathematics, or another quantitative discipline
• Experience with Apache Spark, Databricks, Power BI, or similar analytics and big data technologies
• Knowledge of infrastructure as code (IaC) tools and cloud-native data architectures
• Experience optimizing data pipelines, workflows, and distributed computing environments for performance and scalability