Senior Data Engineer, Data Integration & Performance Platform (12-Month Contract)

Choose a workplace that empowers your impact.

Join a global workplace where employees thrive. One that embraces diversity of thought, expertise and experience. A place where you can personalize your employee journey to be — and deliver — your best.

We are a purpose-driven, dynamic and sustainable pension plan. An industry leading global investor with teams in Toronto to London, New York, Singapore, Sydney and other major cities across North America and Europe. We embody the values of our 665,000 members, placing their best interests at the heart of everything we do.

Join us to accelerate your growth & development, prioritize wellness, build connections, and support the communities where we live and work.

Don’t just work anywhere — come build tomorrow together with us.

Know someone at OMERS or Oxford Properties? Great! If you're referred, have them submit your name through Workday first. Then, watch for a unique link in your email to apply.

This is a senior engineering role on a multi‑year investment platform transformation that is replacing our core investment book of record with BlackRock Aladdin and building a new cloud data platform on Snowflake. We are not staffing a ticket‑taking function. We are building a data capability that has to run reliably, scale efficiently, and remain operable long after go‑live.

You will sit inside the Investment Finance and Operations Platforms group and work across four delivery areas. The program is fast‑moving and priorities shift, so you should expect to move between these areas as the work demands:

Conversion. Build and run data pipelines that convert holdings, transactions, and reference data from legacy platforms into Aladdin, including reconciliation logic and break resolution through parallel run.

Data Integration. Design and maintain system and vendor integrations into and out of Aladdin across order management, treasury, private markets, and market data providers.

Data Platform. Own the build‑out of our Snowflake‑based Integrated Data Platform using a medallion pattern, including data modelling, pipeline orchestration, data quality controls, and end‑to‑end lineage.

Custom Solutions. Engineer data services and APIs that close gaps between Aladdin native functionality and what our investment, risk, performance, and finance teams require.

This team also operates the current production investment data platforms, spanning performance, accounting, order management, and integration. You will spend time working in that environment, because you cannot design the target state without understanding what today’s platforms actually do, which business processes depend on them, and which data behaviours must be preserved through the transition. That current‑state knowledge is a deliberate part of the role, not a distraction from it. Adaptability is the single most important attribute here, and you should expect to move between delivery areas as priorities shift.

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 clear 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 escalate 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 transformation programs.

SQL and data modelling. Expert‑level SQL and strong working knowledge of relational and dimensional data modelling. This will be assessed. You should be able to write complex joins, window functions, and aggregations; profile dat