Senior Lead Data Engineer (Remote)

Stryker•Chicago, MI

7d•$118,000 - $255,700•Remote

About The Position

As a Senior Lead, Data Engineering, you will serve as a technical leader who helps shape the future of enterprise data solutions. In this role, you will drive complex data initiatives, influence technical strategy, and partner with teams across the organization to build scalable, high-impact data products. This is an opportunity to solve challenging business problems while mentoring fellow engineers and elevating data engineering best practices.

Requirements

Bachelor’s degree in Computer Science, Data Engineering, Data Science, Information Systems, Mathematics, Statistics, or a related technical field.
6+ years of experience in data engineering, analytics engineering, software engineering, or enterprise data platform development.
Proven experience architecting, building, and modernizing scalable cloud-based data platforms in an enterprise environment.
Hands-on experience with Azure-based data engineering technologies, including Databricks, Delta Lake, Azure Data Factory, Azure DevOps, CI/CD pipelines, and infrastructure automation.
Strong proficiency in SQL and Python, with experience using Spark or similar distributed data processing frameworks.
Experience designing reliable ETL/ELT pipelines, data warehouse or Lakehouse architectures, data models, and performance-optimized analytics solutions.
Experience integrating data from multiple ERP or enterprise source systems and harmonizing inconsistent master, supplier, purchasing, or transactional data into common data models.
Experience with API integrations, REST services, authentication, security, data governance, and production support practices.
Demonstrated ability to establish engineering standards, conduct architecture reviews, improve documentation, mentor engineers, and influence technical direction without direct authority.
Ability to partner with analysts, engineers, architects, and business stakeholders to translate complex business requirements into scalable, maintainable engineering solutions.

Nice To Haves

Master’s degree in Computer Science, Data Engineering, Data Science, Information Systems, Business Analytics, or a related technical field.
Experience supporting procurement, supply chain, manufacturing, finance, or ERP analytics in a global enterprise environment.
Experience building or modernizing enterprise data platforms, Lakehouse architectures, or cloud analytics ecosystems at scale.
Hands-on experience with multiple ERP platforms, such as SAP ECC, SAP S/4HANA, Oracle, JD Edwards, Infor, QAD, or Microsoft Dynamics.
Full-stack engineering experience, including React, Next.js, Vercel, API development, authentication, and internal web application development.
Experience using AI-assisted engineering tools and workflows, such as GitHub Copilot, ChatGPT, Claude, Cursor, AI coding agents, AI-assisted testing, or AI-assisted documentation.
Experience designing internal tools, data products, APIs, or analyst-facing applications that improve productivity and simplify access to insights.
Experience leading cloud modernization, platform migration, technical debt reduction, automation, or data quality improvement initiatives.
Experience with Power BI, Tableau, or similar business intelligence and visualization tools.
Demonstrated curiosity and continuous learning mindset, with a track record of experimenting with emerging technologies and applying them to practical engineering or analytics use cases.

Responsibilities

Lead the architecture, development, and modernization of scalable enterprise data platforms that support global procurement analytics and business transformation.
Define and help execute a multi-year data engineering strategy focused on platform scalability, reliability, automation, technical debt reduction, and long-term maintainability.
Design, build, and optimize Azure-based data solutions using technologies such as Databricks, Delta Lake, Azure Data Factory, Azure DevOps, CI/CD pipelines, and infrastructure automation.
Integrate and harmonize data across multiple ERP systems by standardizing supplier, purchasing, and master data into common enterprise data models.
Partner with procurement analysts, architects, engineers, and business stakeholders to translate complex business needs into reusable, scalable data products and engineering solutions.
Establish engineering standards, conduct architecture reviews, improve documentation, and mentor engineers to raise the overall technical capability of the team.
Identify and implement AI-enabled approaches that accelerate development, improve data quality, automate documentation, support testing, and enhance analyst productivity.
Evaluate and recommend tools, frameworks, patterns, and platform investments that improve performance, reliability, security, governance, and operational efficiency.
Support the development of internal data products, APIs, and user-facing applications that simplify access to procurement insights and improve analyst productivity.
Present technical recommendations, roadmap priorities, tradeoffs, and business impacts to technical and non-technical stakeholders, including leadership teams.