Company Overview:
Bridgenext is a digital consulting services leader that helps clients innovate with intention and realize their digital aspirations by creating digital products, experiences, and solutions around what real people need. Our global consulting and delivery teams facilitate highly strategic digital initiatives through digital product engineering, automation, data engineering, and infrastructure modernization services, while elevating brands through digital experience, creative content, and customer data analytics services.
Don’t just work, thrive. At Bridgenext, you have an opportunity to make a real difference - driving tangible business value for clients, while simultaneously propelling your own career growth. Our flexible and inclusive work culture provides you with the autonomy, resources, and opportunities to succeed.
Position Description:
We are seeking skilled Data Engineers with Full Stack development to support high-impact data and AI initiatives within the Global Wealth and Asset Management (GWAM) Data division of our Financial Services Client. This role will be critical in enabling the migration to Databricks Unity Catalog, building reusable data products, and supporting application-level integrations for the advanced analytics team to build agents.

Key Responsibilities:

• Design and build data pipelines (batch-first, with increasing move toward stream processing) on Databricks Lakehouse
• Support Unity Catalog migration from legacy Databricks setups
• Integrate data services with front-end applications using APIs
• Participate in end-to-end delivery of data products (ingestion, transformation, storage, and consumption)
• Collaborate with platform, app, and AI teams to deliver reusable frameworks for use cases like call center transcript analytics

Workplace: Remote role based in Ontario with some travel to client site in Toronto on as needed basis

MUST HAVE SKILLS:

• Strong experience with Databricks (including Lakehouse, Delta Lake, Unity Catalog)
• Hands-on experience with batch data pipelines, and working knowledge of streaming frameworks (e.g., Spark Structured Streaming)
• Proficient in PySpark, Python, SQL, and data transformation logic
• Experience with API development and integration (to connect data layers with apps)
• Ability to collaborate across data engineering, full-stack dev, and AI/ML teams
• Understanding of data product principles and platform-as-a-product models
• Good understanding of data governance, access control, and security models

PROFESSIONAL SKILLS:

• Solid written, verbal, and presentation communication skills
• 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

• Design and build data pipelines (batch-first, with increasing move toward stream processing) on Databricks Lakehouse
• Support Unity Catalog migration from legacy Databricks setups
• Integrate data services with front-end applications using APIs
• Participate in end-to-end delivery of data products (ingestion, transformation, storage, and consumption)
• Collaborate with platform, app, and AI teams to deliver reusable frameworks for use cases like call center transcript analytic