Data Warehouse Engineer

Mastronardi Produce•Kingsville, ON

2d•CA$85,000 - CA$95,000•Onsite

About The Position

Mastronardi Produce, a leading greenhouse vegetable company, is seeking a Data Warehouse Engineer for its head office in Kingsville, ON. This role involves enhancing and maintaining the company's One Data Platform (ODP), which is built on Microsoft Fabric using Medallion architecture. The primary development is done in PySpark notebooks, requiring strong coding skills. The engineer will be responsible for profiling and analyzing data to meet stakeholder requirements, building and optimizing pipelines, and designing data models for end-user consumption and reporting. The position demands strong SQL, Spark/Python, and data modeling expertise, sound engineering judgment, and the ability to guide other data engineers.

Requirements

Bachelor’s degree in computer science, information systems, or a related field.
Strong Hands-on experience with Microsoft Fabric (Lakehouses, Data Pipelines/Dataflows, Notebooks) and strong working knowledge of Medallion architecture (Bronze/Silver/Gold layer design).
Good hands-on experience with Synapse, Amazon, S3, Azure Data Lake Storage or Google Cloud Storage.
Expert-level SQL, including complex joins, window functions, and query performance tuning, with the ability to design SQL-first data models that business users and report developers can query directly and confidently.
Strong data modeling skills, including dimensional (star schema) modeling, grain definition, and slowly changing dimensions, with an emphasis on designing schemas that are both engineering-sound and easy for end users to navigate.
Expert-level PySpark and Python, as most Fabric development is done in Spark notebooks — including building and optimizing transformations, and writing clean, maintainable, production-grade notebook code.
Demonstrated ability to profile unfamiliar data sources and recommend the right ingestion and refresh strategy based on source constraints, volume, and business need.
Experience identifying performance bottlenecks and reducing pipeline run times in a production data engineering environment.
Strong root-cause analysis and troubleshooting skills for data quality and pipeline issues.
Experience gathering requirements directly from business stakeholders and partnering with report developers to validate data accuracy and performance.
Experience reviewing others’ technical work and providing constructive, actionable guidance, even without formal management authority.

Nice To Haves

Experience with other big data or cloud platforms (e.g., Hadoop, Databricks, AWS, Azure, or GCP) is a plus, though the core platform for this role is Microsoft Fabric.
Experience using AI coding assistants and LLM-based tools (e.g., Claude, GitHub Copilot) to accelerate data engineering work, writing and reviewing SQL/PySpark, debugging pipelines, and generating documentation while retaining full ownership of correctness, performance, and quality.
Familiarity with real-time/streaming data processing (e.g., Kafka, Flink, Fabric Eventstream) is a plus.
Familiarity with data governance frameworks, data quality management, and metadata/catalog tools.
Experience with version control (GIT) and agile development practices.
Certifications in relevant technologies (e.g., Fabric Analytics Engineer. AWS Certified Big Data Specialty) are a plus.

Responsibilities

Profile and analyze data to translate business and reporting requirements into performant, well-modeled Gold-layer datasets.
Profile new data sources and determine the appropriate ingestion method and refresh approach (full load, incremental, CDC, or near-real-time) based on source system constraints, data volume, and business need.
Design, build, and continuously optimize data pipelines, with a focus on reducing pipeline run times and improving reliability at scale.
Own the investigation, root-causing, and resolution of data engineering-related pipeline and data issues, partnering with source system owners where needed.
Build and evolve a data foundation that is flexible and easy for end users to work with directly — enabling them to write their own SQL and build their own reports against governed, well-documented Gold-layer models.
Work closely with report developers throughout the build process to ensure requirements are met and that final data is accurate, well-modeled, and performant.
Partner with stakeholders across Finance, Supply Chain, Sales, Operations, and Logistics to understand data requirements and business logic, and translate them into scalable data models.
Review the work of other data engineers and provide guidance on approach, technique, and best practices, helping raise the overall quality and consistency of engineering across ODP.
Think across projects and domains — rather than in isolation — to build and enhance a data foundation that scales as one cohesive platform instead of disconnected, duplicative solutions.
Implement data governance, security, and access control practices consistent with ODP standards, and maintain clear documentation for data models, pipelines, and workflows.
Operate as a team player who executes on what’s best for the business, collaborating effectively with engineers, report developers, and stakeholders in a fast-paced, evolving environment.