Manager Data Engineering
About The Position
The Data Engineering Manager will play a pivotal role in shaping the data engineering strategy and technical vision that powers digital products across the Orthopaedics Supply Chain. This role will lead the Data Foundation initiative to modernize the current data landscape and build a scalable lakehouse architecture using Databricks and cloud technologies. The manager will drive architecture, engineering standards, governance, and execution to deliver trusted, reusable, and business-ready data products. Additionally, they will provide technical leadership for business separation activities, ensuring that data architecture, pipelines, and platforms are designed to support future-state organizational needs while maintaining continuity, scalability, and delivery excellence. This position requires strong leadership across architecture design, engineering governance, vendor delivery, and cross-functional collaboration to translate strategy into measurable business outcomes. The role is ideal for someone energized by building modern data capabilities that improve healthcare operations and enable better decisions at scale, offering an opportunity to make a meaningful impact.
Requirements
- A Bachelor’s or Master’s degree in Computer Science, Engineering, Applied Mathematics, or in a related field.
- Minimum of 8 years of experience in data engineering, including 2 or more years in leadership or people management roles
- Strong hands-on technical experience with: Databricks, PySpark, Spark SQL, Python and SQL development, Cloud platforms (Azure preferred, AWS acceptable)
- Proven expertise in: Modern data architecture and platform design, Dimensional modeling and Data Vault 2.0
- Experience with: dbt, Airflow, Azure Data Factory (or equivalent tools), CI/CD pipelines, automation frameworks, and testing practices
- Required experience designing and implementing automated testing strategies for data engineering pipelines, including: End-to-end pipeline validation, Data quality and integrity testing, Integration with CI/CD pipelines
- Experience leading distributed teams and partnering effectively with external vendors and cross-functional stakeholders
Nice To Haves
- Experience supporting Agile product delivery teams and mentoring engineers in complex, fast-paced technical environments
- Familiarity with end-to-end supply chain domains such as planning, manufacturing, procurement, and distribution within MedTech or a similar regulated industry
- Knowledge of enterprise systems of record such as ERP, MES, and PLM, including experience working with fragmented or legacy data environments
- Experience with: Lakehouse architecture and Delta Lake, Data observability and monitoring frameworks
- Experience collaborating with AI/ML and advanced analytics teams to enable scalable data and model-ready pipelines
- Knowledge of Power BI or enterprise BI platform
- Experience leveraging AI-assisted development tools (e.g., GitHub Copilot, Cursor, or similar) to: Accelerate pipeline development, Automate engineering workflows
- Exposure to code generation, pipeline automation, or low-code data engineering accelerators
Responsibilities
- Lead the Data Foundation initiative to modernize the enterprise data ecosystem through a scalable lakehouse architecture and cloud-based data platform capabilities
- Define the data engineering strategy, target architecture, and reusable pipeline frameworks needed to deliver governed, high-quality data products
- Develop the strategy for a Data Supermarket that delivers business-ready data products for use across multiple functions
- Translate complex business requirements and technical challenges into scalable architecture decisions and executable delivery plans
- Provide technical leadership for business separation activities, ensuring alignment to future-state operating models and platform continuity
- Establish and enforce best practices for: Data modeling (dimensional, Data Vault 2.0), Pipeline design, modularity, and reuse, Engineering standards and quality controls
- Establish and scale data governance, data quality, and observability practices, including monitoring, lineage, reliability, and service-level expectations
- Define and implement automated testing strategies for data pipelines, including validation, data quality controls, and CI/CD integration
- Lead development and orchestration using Databricks, Python, SQL, dbt, Airflow, and cloud-native tools
- Partner with vendors and internal teams to manage delivery, enforce standards, and drive outcome-based execution
- Collaborate across Product, Supply Chain business, AI/ML, Data Governance, and IT teams to deliver measurable business impact
Benefits
- Consolidated retirement plan (pension)
- Savings plan (401(k))
- Long-term incentive program
- Vacation – 120 hours per calendar year
- Sick time - 40 hours per calendar year (varies by state)
- Holiday pay, including Floating Holidays – 13 days per calendar year
- Work, Personal and Family Time - up to 40 hours per calendar year
- Parental Leave – 480 hours within one year of the birth/adoption/foster care of a child
- Bereavement Leave – 240 hours for an immediate family member: 40 hours for an extended family member per calendar year
- Caregiver Leave – 80 hours in a 52-week rolling period
- Volunteer Leave – 32 hours per calendar year
- Military Spouse Time-Off – 80 hours per calendar year
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What This Job Offers
Job Type
Full-time
Career Level
Manager
