Data Engineer

The Data Engineer at Bumble Bee Seafoods is responsible for designing, building, and operating the enterprise data platform that powers analytics, reporting, and advanced data use cases across the organization. This role focuses on data ingestion, transformation, modeling, and platform reliability, with Microsoft Fabric as the primary data engineering and analytics platform.

The Data Engineer will work closely with analytics, IT, and business stakeholders to integrate data from ERP, supply chain, and operational systems into governed, scalable, and high‑performance analytical data assets. This role emphasizes strong engineering discipline, data architecture, and operational excellence rather than ad‑hoc analysis or report creation.

Key Responsibilities:

The Data Engineer partners with analytics and business teams to translate data requirements into robust, production‑grade data solutions. This role requires deep technical expertise in data pipelines, storage, modeling, and performance optimization, with a focus on building reusable and future‑ready data assets.

• Design, build, and maintain end‑to‑end data pipelines using Microsoft Fabric and Azure Synapse, including ingestion, transformation, orchestration, and storage.
• Develop and manage Fabric Lakehouse and Warehouse architectures to support enterprise analytics and downstream consumption.
• Implement scalable data ingestion from ERP, supply chain, and operational systems using Fabric Pipelines, Dataflows Gen2, and related tooling.
• Apply strong data modeling and data engineering best practices, including dimensional modeling, normalization where appropriate, and performance optimization.
• Create and maintain curated analytical data layers that serve as trusted sources for Power BI semantic models and other consumers.
• Ensure data quality, reliability, and consistency through validation, monitoring, and structured error handling.
• Collaborate with analytics and BI teams to support Power BI semantic models, focusing on data structures, performance, and governance rather than report design.
• Partner with IT security and infrastructure teams to implement role‑based access control, data security, and compliance standards.
• Establish and maintain documentation for data pipelines, schemas, transformations, and architectural decisions.
• Support deployment, versioning, and lifecycle management of data assets across development, test, and production environments.
• Continuously evaluate and adopt new Microsoft Fabric capabilities to improve scalability, performance, and maintainability.
• Contribute to a collaborative, engineering‑focused culture that values clean design, automation, and long‑term platform health.

Qualifications and Skills Desired:

Required

• Bachelor’s degree in computer science, Data Engineering, Information Systems, or a related quantitative field.
• Strong experience with data engineering concepts, including ETL/ELT, orchestration, data modeling, and pipeline design.
• Proficiency in SQL for data transformation, validation, and performance tuning.
• Hands‑on experience with Microsoft Fabric and Azure Synapse Analytics.
• Solid understanding of relational and analytical data modeling techniques.
• Experience working with large, complex datasets from multiple source systems.
• Strong problem‑solving skills and an engineering mindset focused on reliability and scalability.
• Ability to collaborate effectively with analytics, IT, and business stakeholders.
• Experience with Python is a must.

Preferred

• Experience with Microsoft Fabric Lakehouse, Warehouse, Dataflows Gen2, Pipelines, and Notebooks.
• Understanding of foundational data architecture concepts such as medallion architecture design
• Familiarity with Power BI semantic models from a data engineering and performance perspective.
• Experience integrating data from SAP S/4
  
  HANA or similar ERP systems.
• Understanding of supply chain or manufacturing data domains.
• Exposure to data governance concepts such as lineage, certification, and access control.
• Experience with automation, CI/CD concepts, or infrastructure‑as‑code for data platforms.
• Exposure to machine learning or advanced analytics pipelines is a plus.
• Experience with Azure Dev Ops…