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

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.

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.

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.

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.

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.

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.

Education and certification. Bachelor’s degree in Computer Science, Engineering, Mathematics, or related field. Cloud certifications (Azure, Snowflake) or data engineering certifications are an asset but are not a substitute for demonstrated delivery.

Communication. You can hold your own with a portfolio manager, an operations lead, and a data architect in the same conversation, and you write clearly enough that your technical decisions survive without you in the room.

Tooling curiosity. Experience using generative AI tools and prompt engineering to accelerate analysis, documentation, and code generation.

You have taken ownership of at least one data pipeline or integration capability, and stakeholders go to you directly rather than through a project manager when they have data questions.

At least one set of related integration requests has been consolidated into a single reusable data product instead of several point‑to‑point pipelines.

Anything you have delivered has a documented support model, automated monitoring, and data quality dashboards, and can be operated without you.

You can independently investigate a data discrepancy end‑to‑end, from the business question through to the source system, without escalating to an engineer to run the query.

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.

As one of Canada’s largest defined benefit pension plans, our people-first culture is at its best when our workforce reflects the communities where we live and work — and the members 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.