Data Engineer

Title: Data Engineer

Location: Nashua NH

The Data Engineer will support the Information Systems Lead by building and maintaining the data pipelines, integrations, lakehouse structures, and curated data products required for scalable analytics, automation, and AI readiness across CBS. This role is responsible for turning fragmented business data from source systems into reliable, governed, and reusable datasets. The Data Engineer will work closely with the IS Lead, Data Analyst, Business Analyst, corporate IT, implementation partners, functional data owners, and the AI Solutions & Automation team. The role is focused on source-system onboarding, ETL/ELT pipelines, data quality, orchestration, medallion architecture, and production data reliability. AI solution design and business workflow automation will be owned by the AI Solutions & Automation team, with the Data Engineer providing the trusted data foundation required to support those solutions.

RESPONSIBILITIES: Design, build, test, and maintain scalable data pipelines from priority source systems into the CBS cloud/lakehouse environment.

Integrate data from enterprise and functional systems including ERP, MES, PLM, QMS, SI/test data, SharePoint/file repositories, Azure SQL, and other business platforms.

Implement ETL/ELT patterns, data transformations, and medallion architecture layers including raw, cleaned, curated, and business-ready datasets.

Develop data quality checks, validation logic, reconciliation methods, exception handling, and monitoring to ensure trusted and reliable data products.

Support secure on-prem-to-cloud data integration patterns including database/API connectivity, file-based ingestion, scheduled refreshes, and batch or event-based processing.

Partner with the IS Lead to develop data models, semantic layers, standardized KPIs, and reusable data assets that support dashboards, analytics, automation, and AI use cases.

Partner with the Data Analyst and Business Analyst to understand reporting requirements, business definitions, and functional data needs.

Support the AI Solutions & Automation team by preparing AI-ready datasets, defining data access patterns, and ensuring data products are governed, reliable, and reusable.

Build and maintain orchestration, scheduling, logging, alerting, and pipeline documentation to support production operations and sustainment.

Collaborate with corporate IT, security teams, and data owners to implement access controls, data lineage, naming standards, environment controls, and data governance requirements.

Support implementation partners and vendors during architecture, pipeline build, source-system integration, testing, documentation, and handoff activities.

Troubleshoot data issues, performance problems, refresh failures, and source-system changes that affect downstream analytics or automation capabilities.

Contribute to continuous improvement of data engineering standards, development practices, documentation, and reusable pipeline patterns.

Travel as required to support global operations and source-system discovery or deployment activities.

Perform other duties and responsibilities as required to support the growth and success of the business.

QUALIFICATIONS: BS in Computer Science, Information Systems, Data Engineering, Data Analytics, Engineering, or related field.

3-7+ years of experience in data engineering, analytics engineering, ETL/ELT development, data integration, or cloud data platform implementation.

Strong SQL skills and experience building data models, transformations, joins, validations, and production-ready datasets.

Experience with Python, PySpark, notebooks, or comparable data engineering tools strongly preferred.

Experience with modern cloud data platforms such as Microsoft Fabric, Azure Data Lake, Azure Data Factory, Synapse, Databricks, Snowflake, or comparable platforms.

Familiarity with lakehouse architecture, medallion data structures, semantic models, data quality controls, pipeline orchestration, and data governance concepts.

Experience integrating data from ERP, MES, PLM, QMS, manufacturing, engineering, or operational systems strongly preferred.

Experience with APIs, file ingestion, database extraction, scheduled refreshes, and on-prem-to-cloud data movement preferred.

Familiarity with Power BI, semantic models, DAX, or business intelligence consumption patterns preferred.

Experience with Git, DevOps practices, deployment management, testing, monitoring, and documentation preferred.

Strong analytical, troubleshooting, communication, and problem-solving skills.

Ability to work with business stakeholders, technical teams, and external partners to translate source-system data into reliable business-ready assets.

Travel required as needed to support global sites and data integration activities.