Operations Data Engineer (On Site Only)

ZOLL Medical•Pittsburgh, PA

1d•Onsite

About The Position

The Operations Data Engineer supports ZOLL's smart manufacturing and data-driven transformation efforts by designing, building, and maintaining the data infrastructure needed for reporting, analytics, automation, and operational decision-making. This role owns the Operations data warehouse, develops and supports data pipelines, integrates manufacturing and enterprise systems, and curates trusted datasets for dashboards and analytics. The Operations Data Engineer is expected to be a versatile technical contributor who can adapt to evolving business needs and support a broad range of data engineering, automation, digital manufacturing, and advanced analytics initiatives.

Requirements

Bachelor's degree in Information Systems, Computer Science, Engineering, Data Engineering, Data Analytics, or a related technical discipline or a compelling combination of education, certifications, and experience required
5+ years of relevant experience designing, implementing, and supporting production data solutions in manufacturing, engineering, industrial, or similarly complex operational environments required.
Proficiency with SQL Server, relational database design, data modeling, stored procedures, query optimization, and schema development required.
Experience developing ETL/ELT solutions and data pipelines required
Proficiency with Python or a similar scripting language for automation, transformation, and integration tasks required.
Understanding of data governance, metadata, lineage, cybersecurity, and documentation best practices required.
Understanding the basics of statistical analysis, machine learning concepts, or predictive modeling methods required.
Strong problem-solving and communication skills with the ability to translate technical concepts into practical business solutions required.

Nice To Haves

Master’s degree preferred
experience with Azure Data Factory or similar integration tools preferred.
Experience designing, maintaining, or supporting data warehouse environments, including dimensional modeling, data marts, or semantic layers strongly preferred.
Experience working with machine, test, process, traceability, or time-series manufacturing data strongly preferred.
Familiarity with MES, ERP, SCADA, LabVIEW, IoT, or other manufacturing systems and with industrial integration technologies such as Kepware, OPC-UA, historians, or streaming architectures preferred.
Demonstrated experience building and supporting production-ready solutions that are actively used by business stakeholders preferred.
Familiarity with reporting and visualization platforms such as SSRS, Power BI, Grafana, Tableau, or similar tools preferred.
Project management experience and demonstrated ability to lead cross-functional data initiatives preferred.
Familiarity with Lean, Six Sigma, or Agile/Scrum methodologies preferred.
Experience in FDA-regulated, medical device, or other highly regulated manufacturing environments preferred.

Responsibilities

Design, develop, and maintain data pipelines connecting MES, ERP, SCADA, LabVIEW, IoT, and other operational systems.
Serve as technical owner of the Operations data warehouse, including data models, schemas, performance optimization, reliability, and availability.
Develop and maintain scalable ETL/ELT processes that transform and publish trusted operational data.
Implement and support time-series data ingestion, storage, retention, and analytics capabilities for manufacturing and test systems.
Use SQL, Python, and related tools to automate data transformation, validation, monitoring, and documentation processes.
Curate and publish trusted datasets for dashboards, reporting, analytics, and business intelligence applications.
Establish and support data quality, governance, lineage, metadata, access control, and cybersecurity practices.
Support integration and digital transformation initiatives by improving data availability, accessibility, and reliability across Operational Systems.
Evaluate emerging technologies, architectures, and connectivity solutions that improve scalability, maintainability, or cost effectiveness.
Develop working knowledge of advanced analytics, machine learning, and AI-enabled workflows to provide flexible support as Operational Systems capabilities evolve.
Maintain documentation of data sources, relationships, transformation logic, business rules, and validation methods to support sustainability and knowledge transfer.
Independently investigate and solve complex or ambiguous data and system challenges with limited direction.