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

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.