Senior Data Engineer – Data Products & Modeling

Bridgenext Digital Engineering•Toronto, ON

1d•Hybrid

About The Position

This is a blended Data Engineering and Data Modeling role responsible for designing enterprise data structures and engineering Azure-based data products that are reliable, scalable, and analytics-ready. The role owns the full lifecycle of data products from translating business concepts into well-defined data models, to engineering pipelines that deliver curated, reusable, and trusted data products for analytics, reporting, and downstream integration. The ideal candidate is equally strong in data design, pipeline engineering, and product thinking, with a focus on delivering high-quality data as a product to consumers across the organization.

Requirements

Bachelor’s degree in Computer Science, Information Systems, or a related field
8+ years of experience spanning data engineering and data modeling
Strong hands-on experience with Databricks on Microsoft Azure or AWS
Proven expertise with Erwin and/or PowerDesigner
Solid understanding of data warehousing, dimensional modeling, and analytics design patterns
Experience delivering data using data product–oriented approaches (curated layers, semantic models, governed datasets)
Experience implementing CI/CD and DevOps for data platforms
Strong communication skills with the ability to clearly articulate data product design and usage to technical and non-technical audiences

Nice To Haves

Strong team and individual player
Maintains composure during all types of situations and is collaborative by nature
High standards of professionalism, consistently producing high quality results
Self-sufficient, independent requiring very little supervision or intervention
Demonstrate flexibility and openness to bring creative solutions to address issues

Responsibilities

Partner with business stakeholders and analytics teams to define data products that align to business domains, use cases, and outcomes
Translate business requirements into well-defined data entities, relationships, and metrics, and engineer data products that implement those models end to end
Design and maintain conceptual, logical, and physical data models that underpin scalable and reusable data products
Build and optimize batch and near–real-time data pipelines in Azure using Azure Data Factory, Azure Databricks (PySpark), Azure Data Lake, and Azure SQL
Engineer curated, analytics-ready data products optimized for BI, reporting, APIs, and OLAP consumption (e.g., Power BI)
Create, manage, and evolve data models using ERwin or PowerDesigner, ensuring consistency and alignment across data products
Define and maintain data product metadata, including data definitions, lineage, ownership, and quality expectations
Perform reverse engineering and model reconciliation to align existing datasets and pipelines with logical models and product intent
Apply CI/CD and DevOps practices to data pipelines, schemas, and data products using Azure DevOps, GitHub, or Jenkins
Collaborate closely with engineers, architects, analysts, and product teams to ensure data products are discoverable, trusted, and well understood
Identify opportunities to standardize, reuse, and evolve data products across domains and initiatives
Mentor team members and contribute to enterprise standards for data modeling, engineering, and data product delivery