Data Engineer

The Data Engineer is responsible for the design, development, and operation of DuBois Chemicals’ data pipelines to support enterprise analytics, reporting, and AI initiatives. This role plays a critical part in transforming transactional and unstructured data into trusted, analytics‑ready datasets that enable informed decision‑making across Sales, Manufacturing, and Finance. This is a hands‑on, senior individual contributor role requiring deep technical expertise, strong collaboration skills, and the ability to operate in a fast‑paced environment. The Data Engineer partners closely with analytics, BI, and business stakeholders to ensure data assets are scalable, governed, reliable, and aligned to business needs. KEY RESPONSIBILITIES: Design and implement scalable dimensional data models, including fact tables, dimension tables, star schemas, and semantic business layers. Continuing enterprise standards for data modeling, naming conventions, transformations, and reusable business logic. Partner with business stakeholders to define and govern critical business metrics, hierarchies, and master data concepts. Design analytics-ready data products that support Power BI, AI solutions, and self‑service analytics. Snowflake & dbt Engineering Design, build, and operate reliable ingestion pipelines across multiple data sources. Design, develop, and maintain dbt transformation frameworks following data engineering best practices. Develop modular, reusable, and testable dbt models to standardize business logic across the enterprise. Optimize Snowflake performance, warehouse utilization, storage architecture, and cost management. Implement automated testing, documentation, lineage, and deployment processes within dbt. Establish CI/CD practices and development standards for enterprise data transformations. Lead migration of legacy SQL Server, Excel, and Alteryx-based processes into modern cloud‑based architectures. Data Quality, Metadata & Reliability Enrich datasets with technical and business metadata, including data definitions, ownership, and lineage. Implement data quality monitoring and controls, including freshness, duplicate detection, schema drift, and anomaly detection. Improve pipeline observability, monitoring, and operational reliability to support production‑grade data workloads. Collaboration & Technical Leadership Partner closely with analytics engineers, BI developers and business stakeholders to deliver trusted data solutions. Contribute to shared standards, patterns, and best practices for data engineering and analytics enablement. Provide technical leadership and mentorship appropriate for a senior, hands‑on engineering role without direct people management responsibilities. MINIMUM QUALIFICATIONS: 3-5 years of technical, hands‑on experience as a Data Engineer working with both transactional and analytical data. Deep understanding of dimensional data modeling. Deep expertise in Snowflake architecture, performance optimization, and data modeling. Strong experience with dbt including model development, testing, documentation, and deployment. Extensive experience designing dimensional models, semantic layers, and enterprise reporting datasets. Experience integrating data from ERP, CRM, and operational systems, preferably Dynamics 365 Finance & Operations and Dynamics 365 CRM. Ability to manage multiple priorities, meet deadlines, and collaborate effectively with technical and non‑technical stakeholders. Strong written and verbal communication skills. PREFERRED QUALIFICATIONS: Experience with Microsoft Dataverse, OneLake Experience developing analytics solutions for Power BI. Experience to Snowflake Cortex or similar AI‑enabled data platform capabilities. Experience implementing CI/CD pipelines and DataOps practices. Familiarity with building custom agents using Copilot Studio or Azure AI Foundry. EDUCATION QUALIFICATIONS: Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field, or equivalent practical experience. #J-18808-Ljbffr CBTS