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We are a leading global real estate investor, developer and manager. We combine our capital with our capabilities to create real estate that strengthens economies and communities. By prioritizing people, partnerships and places, we generate meaningful returns for OMERS members, enhance value for our capital partners and create a brighter world for our customers.
Join us to accelerate your growth & development, prioritize wellness, build connections, and support the communities where we live and work.
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We are seeking a Data Engineer to help design, build, and scale an enterprise data platform on Microsoft Fabric and complementary Azure data services. This role focuses on delivering high‑quality, insight‑ready data products using a Medallion architecture that supports analytics, reporting, and AI‑driven decision‑making.
A key aspect of this role is working with both structured and unstructured data—including documents, PDFs, scanned files, and text‑heavy content—and transforming them into trusted, auditable datasets that can be consumed by analytics and AI solutions.
We believe that time together in the office is important for OMERS and Oxford, the strength of our employees, and the work we do for our pension members. Our hybrid work guideline requires teams to come to the office a minimum of 4 days per week.
Key Responsibilities
Data Engineering & Platform Delivery
Design, build, and maintain end‑to‑end data pipelines using Microsoft Fabric (Data Pipelines, Lakehouse/Warehouse, Notebooks) and, where appropriate, Azure Data Factory patterns.
Implement scalable data transformations aligned to a Medallion architecture, producing curated, analytics‑ready datasets.
Support environment separation (Dev / UAT / Prod), Git‑enabled development, and CI/CD‑style deployment practices.
Unstructured Data Processing (Core Focus)
Build ingestion and processing workflows for unstructured and semi‑structured content, including:
PDFs, scanned documents, reports, agreements, and attachments
Text‑heavy or non‑tabular source files
Enable document understanding pipelines that support:
Text extraction (including OCR where required)
Document classification and attribute extraction
Standardization and enrichment of extracted data
Integrate unstructured data outputs with structured datasets to create a unified, trusted analytical view.
Design solutions with strong auditability, traceability, and exception handling, ensuring confidence in downstream insights.
Insight‑Driven Data Modeling
Translate business questions into well‑designed data structures that support analysis, comparison, and trend detection.
Create curated datasets and models optimized for Power BI, semantic models, and AI consumption.
Ensure clarity in data grain, metric definitions, and relationships so insights are explainable and defensible.
Data Quality, Governance & Reliability
Implement data quality checks, reconciliation logic, and validation rules across pipelines.
Monitor pipeline health, data anomalies, and processing exceptions.
Contribute to common engineering standards, reusable patterns, and clear documentation to support scale and consistency.
Collaboration & Enablement
Work closely with analytics, AI, and business teams to co‑design data products that meet real business needs.
Support federated teams by creating reusable components, templates, and best‑practice guidance.
Communicate technical choices and data limitations clearly to both technical and non‑technical stakeholders.
Required Qualifications
Experience
4–7+ years of experience in data engineering or analytics engineering roles.
Hands‑on experience with Microsoft Fabric and/or strong background in Azure Data Factory / Azure data platforms, with the ability to apply those skills in a Fabric‑first environment.
Technical Skills
Strong SQL for transformation, modeling, and performance optimization.
Strong Python experience (PySpark preferred).
Proven experience implementing Medallion‑style data architectures for analytics.
Experience delivering production‑grade, reliable, and maintainable data pipelines.
Unstructured Data Experience (Must Have)
Demonstrated experience processing unstructured data (e.g., documents, PDFs, scanned files, text content).
Familiarity with OCR, text extraction, or document parsing concepts.
Experience integrating extracted document data into analytical datasets and reconciling it with structured sources.
Preferred Qualifications
Deep hands‑on experience with Microsoft Fabric Lakehouse, Warehouse, Notebooks, and Pipelines.
Experience with Git‑based development and CI/CD practices for data platforms.
Exposure to data designs that support AI‑enabled analytics or advanced insight generation.
Experience working within enterprise governance, security, and compliance constraints.
What Success Looks Like
Reliable, well‑monitored data pipelines supporting both structured and unstructured sources.
High‑trust, insight‑ready datasets that consistently power analytics and AI use cases.
Clear, auditable data flows that enable confidence in decision‑making.
Reusable patterns and documentation that help teams scale without friction.
Why Join Us
Work on a modern, Microsoft Fabric‑first enterprise data platform.
Solve complex problems involving unstructured data and advanced analytics.
Help shape data engineering standards and practices—not just execute tasks.
This posting is for an existing vacancy.
The expected salary range for this position is $72,000.00 - $108,000.00 per year.
You may also be eligible to receive an annual Incentive Award pursuant to our Short-term Incentive plan and our Long-Term Incentive plan (if applicable), and to participate in our group benefits and retirement plans – details on these elements of compensation are included within OMERS & Oxford offer letters.
Oxford's purpose is to strengthen economies and communities through real estate.
Our people-first culture is at its best when our workforce reflects the communities where we live and work — and the customers we proudly serve.
From hire to retire, we are an equal opportunity employer committed to an inclusive, barrier-free recruitment and selection process that extends all the way through your employee experience. This sense of belonging and connection is cultivated up, down and across our global organization thanks to our vast network of Employee Resource Groups with executive leader sponsorship, our Purpose@Work committee and employee recognition programs.
Artificial intelligence (AI) tools are used to support certain stages of the OMERS recruitment process. While AI assists us in our process, human judgment and decision-making remain central to our candidate experience.