What You Will Own

Data Engineering and Pipeline Development

Design, build, and maintain robust, high‑performance data pipelines using ELT/ETL patterns – incremental loading, change data capture, historisation, and idempotent replay – to ingest, transform, and serve data from source systems (Aladdin, legacy platforms, vendors) into Snowflake.

Implement medallion architecture (bronze/silver/gold) with explicit separation of raw, conformed, and business‑ready layers, ensuring data quality checks are embedded at each stage.

Write production‑grade SQL and Python/PySpark code; optimise query performance, partition strategies, and warehouse sizing for cost and speed.

Own the orchestration layer (e.g., Prefect, Azure Data Factory, or comparable tools) – schedule, monitor, retry, and alert on pipeline failures with clear SLIs and SLOs.

Data Modelling and Architecture

Partner with architects and business analysts to translate business requirements into relational and dimensional data models that support investment analytics, performance, accounting, and risk reporting.

Define and maintain source‑to‑target mappings in collaboration with analysts, and implement them as maintainable, version‑controlled transformations.

Build and enforce data quality controls – including completeness, uniqueness, referential integrity, and business rule validation – with clear dashboards and notification mechanisms.

Establish data lineage across the entire platform, from source systems through to consumption, so that any data point can be traced end‑to‑end.

Solution Ownership and Operability

Own a data capability end‑to‑end rather than a queue of requests. You will define the technical approach with architects, build it with engineers,



see it into production, and stay accountable for whether it runs correctly and efficiently.

Turn multiple similar‑looking integration requests into one extensible data product. Where stakeholders ask for a new report or a specific data extract, you are expected to identify the underlying capability gap and design for the general case so the next request is an extension, not a new pipeline.

Design for operability from day one. Every pipeline you deliver must have a defined support model, monitoring, automated alerts, data quality dashboards, and runbooks so it can be operated by an operations team rather than remaining with the engineers who built it.

Implement Infrastructure‑as‑Code (Terraform, ARM, or comparable) and CI/CD pipelines for data platform components, ensuring repeatable and auditable deployments.

Delivery, Testing, and Quality

Define and execute data engineering test plans, including unit tests, integration tests, performance tests, and regression tests for data pipelines.

Participate in defect triage, root‑cause analysis, and resolution across environments.

Write technical specifications and acceptance criteria that engineers build against and that systems integrators are held to; review vendor deliverables against those criteria.

Identify gaps and elevate where quality or completeness falls short.





Operate across both Agile and waterfall delivery models, and keep scope, deliverables, and technical timelines clearly documented and communicated.

Current State to Target State

Build a working understanding of the current investment data and platform environment, including the processes, calculations, and downstream consumers they support, and use that understanding to define what must be replicated, improved, or retired in the target state.

Support the current environment where doing so builds transition knowledge, including investigating data issues, tracing lineage through existing systems, and validating that behaviour is preserved through migration.

Become a subject‑matter expert on the data platforms in your area across both current and target state, and be the person the business and the delivery teams come to for how the data actually flows and behaves.

Build and maintain technical data documentation, including data dictionaries, lineage diagrams, metadata, and knowledge‑base articles.

Institutional knowledge that lives only in one person’s head is treated as a defect.

Change and Adoption

Apply change management principles throughout delivery, including early capture of stakeholder impacts, end‑user training on new data products, and clear communications, so that what we deliver is actually adopted and trusted.

What You Bring

Required Skills & Experience

- Experience. 6 or more years in data engineering, software engineering, or data platform roles supporting investment platforms, order management, investment accounting, data warehouses, or cloud data platforms, spanning change initiatives and large trans

📌 Senior Data Engineer, Data Integration (Toronto)
🏢 OMERS / Oxford Properties Group
📍 Toronto