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

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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.

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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 data; and investigate performance issues without hand‑holding.
• Modern data stack. Production experience with Snowflake (or equivalent cloud data warehouse), including performance tuning, zero‑copy cloning, time travel, and role‑based access control. Strong working knowledge of Python and data engineering libraries (PySpark, Pandas, Polars, or similar).
• Orchestration and pipelines. Deep understanding of ETL and ELT design patterns, including incremental loading, change data capture, historisation, data quality controls, and idempotency. Experience with orchestration tools such as Prefect, Airflow, Azure Data Factory, or Dagster.
• Integration patterns. Experience building integrations with investment systems (Aladdin, order management, accounting, performance, or market data vendors) and handling varied data formats (JSON, XML, flat files, APIs).
• Investment systems. Demonstrated experience at Senior Data Engineer level or above working directly with investment systems across at least two of the following four areas: performance measurement and attribution, investment accounting, order management, and risk. We do not expect all four, but a candidate whose exposure is limited to a single area is unlikely to be a fit for the breadth of this role.
• Investment domain. Depth in one or more investment data domains: security and reference data, positions and transactions, portfolio accounting, order and trade lifecycle data, performance, or risk analytics. Working knowledge of the investment lifecycle across exchange‑traded and OTC products, including derivatives and structured or look‑through instruments.
• Temperament. Comfortable with ambiguity, shifting priorities, and reprioritisation mid‑sprint. You raise issues early, propose a path forward, and do not wait to be told what to do.
• CI/CD and version control. Strong experience with Git, branching strategies, and CI/CD pipelines for data code (dbt, Azure DevOps, or comparable).

Preferred Skills & Experience

• Product thinking. Evidence that you have owned a data capability over its life rather than delivered a series of discrete pipelines. We will ask you to describe a data product you built, who used it, who ran it afterwards, how it was monitored, and how it was extended.
• Domain depth. Depth in one or more of the following. We do not expect all three:
  • Performance and attribution. Understanding of the data required for performance measurement and attribution across fixed income and equity strategies, beyond familiarity with the systems that consume them.
  • Order management and trade lifecycle. Working knowledge of order management workflows across the trade lifecycle, and the data these processes generate. Experience with Aladdin OMS, Charles River, or comparable platforms is an asset.
  • Investment accounting. Understanding of investment accounting concepts (IBOR/ABOR, accruals, amortisation, corporate actions, NAV/book value) and the data structures behind them.
• Platform exposure. Experience implementing or supporting BlackRock Aladdin, Aladdin Data Cloud, eFront, Charles River, Calypso, Eagle PACE, SimCorp Dimension, FactSet, Bloomberg, or Yardi. We do not expect all of these.
• Modern tooling. Hands‑on experience with dbt (data build tool), Azure (Data Lake, Data Factory, DevOps), Snowflake (advanced), and Prefect or equivalent orchestration.
• Infrastructure as Code. Experience with Terraform, ARM, or Bicep for provisioning cloud resources.

Also Valued

• 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.

What Success Looks Like in the First Six Months

• 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.