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This is an entry‑level 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 looking for someone to simply complete tickets. We are looking for a future senior engineer - someone who wants to learn how enterprise data platforms are built, operated, and scaled, and who is willing to do the hard work of understanding both the data and the business that depends on it.
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. Assist in building and testing data pipelines that convert holdings, transactions, and reference data from legacy platforms into Aladdin, including reconciliation and break resolution through parallel run.
- Data Integration. Help design and maintain system and vendor integrations into and out of Aladdin across order management, treasury, private markets, and market data providers.
- Data Platform. Contribute to the build‑out of our Snowflake‑based Integrated Data Platform using a medallion pattern, including data modelling, data quality controls, and lineage.
- Custom Solutions. Support the design and implementation of data capabilities 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 significant 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 behaviours must be preserved through the transition. That current‑state knowledge is a deliberate part of the role, not a distraction from it.
Adaptability and curiosity are the single most important attributes here. You should expect to move between delivery areas as priorities shift, and you should be excited by the opportunity to learn across the entire investment data lifecycle - not just one narrow silo.
What You Will Own (with Mentorship and Support)
Analysis and Requirements (Foundation Building)
- Work alongside senior data engineers and business analysts to understand business and data requirements. You will learn to read and interpret business requirements documents, user stories, acceptance criteria, business process models, data flow diagrams, and source‑to‑target mapping specifications.
- Perform hands‑on data analysis under guidance. You will query source and target systems to profile data, investigate reconciliation breaks, validate transformations, and prove that what was built is correct - learning to do this independently over time.
- Participate in design sessions and workshops with investment, operations, risk, performance, and finance stakeholders, and learn how to translate between business intent and technical design in both directions.
Capability and Solution Ownership (Growing into It)
- Own a small data capability end‑to‑end with close mentorship. You will help define the problem, shape the solution with engineers and architects, see it into production, and stay accountable for whether it works.
- Learn to turn multiple similar‑looking requests into one extensible capability. Where stakeholders ask for a specific instrument or report, you will be expected to identify the underlying capability gap and design for the general case - so the next request is an extension, not a new build - with coaching from senior team members.
- Design for operability from day one. Every capability you deliver must have a defined support model, monitoring, controls, and documentation so it can be run by an operations team rather than remaining with the engineers who built it. You will learn how to build these from senior engineers.
Delivery, Testing and Quality (Hands‑On Execution)
- Define and execute business test plans, including integration, performance, and regression testing. Participate in defect triage, root‑cause analysis, and resolution across environments.
- Write requirements and acceptance criteria that engineers build against and that vendors are held to, and review systems integrator deliverables against those criteria. Identify gaps and escalate where quality or completeness falls short - you will learn to do this with guidance.
- Operate across both Agile and waterfall delivery models, and keep scope, deliverables, and timelines clearly documented and communicated.
Current State to Target State (Deep Immersion)
- 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 platforms in your area across both current and target state over time, and be someone the business and delivery teams come to for how the data actually behaves - with senior team members backing you up.
- Build and maintain data documentation including data dictionaries, lineage, metadata, and knowledge‑base articles. Institutional knowledge that lives only in one person's head is treated as a defect - and you will be expected to document everything you touch.
Change and Adoption (Learning the Soft Skills)
- Apply change management principles throughout delivery, including early capture of stakeholder impacts, end‑user training, and communications, so that what we deliver is actually adopted.
What You Bring
Required Skills & Experience (Entry‑Level Expectations – We Will Teach the Rest)
- Education. Bachelor's degree in Computer Science, Engineering, Mathematics, Finance, Accounting, Economics, or a related field, graduating in [current year] or within the last 12 months.
- Foundational SQL. Strong working knowledge of SQL - you can write SELECT statements, joins, aggregations, and basic subqueries. You don't need to be expert level yet, but you should be comfortable querying a database. This will be assessed.
- Foundational Python. Working knowledge of Python - you can write functions, use libraries like Pandas, and automate basic data tasks. You don't need to be a senior developer, but you should be able to write clean, readable code.
- Academic exposure to data concepts. You have taken courses or completed projects involving databases, data modelling, ETL/ELT concepts, or cloud computing. You understand what a data warehouse is and why data quality matters.
- Investment domain interest. You have demonstrated interest in investment systems, financial markets, or data platforms through coursework, internships, personal projects, or extracurricular activities. We do not expect deep domain knowledge - we expect curiosity and a willingness to learn fast.
- Temperament. Comfortable with ambiguity, shifting priorities, and reprioritization mid‑sprint. You raise issues early, propose a path forward, and do not wait to be told what to do. You are not afraid to ask questions, but you try to find answers yourself first.
- Tooling curiosity. You have used or are excited to use generative AI tools and prompt engineering to accelerate analysis and documentation - and you understand they are accelerators, not replacements for thinking.
Strongly Preferred Skills (Differentiators)
- Product thinking (evidence of ownership). You have built something over its life - whether an academic capstone project, a personal data pipeline, an internship deliverable, or a hackathon prototype - and you can describe who used it, how it was operated, and how it was extended. We will ask you about this.
- Domain depth (entry‑level exposure). Exposure to one or more of the following through coursework or internships - we do not expect all:
- Performance and attribution. Basic understanding of what performance measurement and attribution are, and the data required to support them.
- Order management and trade lifecycle. Working knowledge of order management workflows across the trade lifecycle (order creation, execution, allocation, confirmation, settlement). Experience with Aladdin OMS, Charles River, or comparable platforms is an asset but not required.
- Investment accounting. Basic understanding of investment accounting concepts, including the IBOR and ABOR distinction, accruals, amortisation, corporate actions processing, and NAV and book value treatment.
- Platform exposure (academic or internship). Experience or exposure to BlackRock Aladdin, Aladdin Data Cloud, eFront, Charles River, Calypso, Eagle PACE, SimCorp Dimension, FactSet, Bloomberg, or Yardi through coursework, internships, or projects. We do not expect all of these.
- Cloud and modern stack (academic or internship). Exposure to Snowflake, Azure, dbt, Prefect, Azure DevOps, or comparable modern data stack tooling - even if only through academic projects or self‑study.
Also Valued
- Certification. CBAP or CFA certification is an asset but is not a substitute for demonstrated delivery. For a new graduate, any cloud certification (Azure, Snowflake) or data‑related certification is a plus.
- Communication. You can hold your own with a portfolio manager, an operations lead, and a data engineer in the same conversation - or you are eager to learn how. You write clearly enough that your requirements survive without you in the room.
- Extracurricular. Leadership in student organizations, hackathon participation, tutoring, or open‑source contributions.
What Success Looks Like in the First Six Months
- You have taken ownership of at least one small data capability or pipeline component, and stakeholders know your name - you are no longer just "the new grad."
- You have contributed to at least one set of related requests that was consolidated into a single reusable solution instead of several point builds, with your senior team members' mentorship.
- Anything you have delivered has a documented support model and can be operated without you - because you learned how to build operability from day one.
- You can independently investigate a data discrepancy end‑to‑end, from the business question through to the source system, with minimal escalation - or you know exactly who to ask and what questions to ask when you get stuck.
- You have built a working understanding of at least one investment data domain (positions, transactions, order lifecycle, performance, or accounting) and can explain it to a non‑technical stakeholder.
- You have become the go‑to person on the team for at least one tool or technology (e.g., SQL optimisation, Python testing, documentation, or lineage tracking).
What You Can Expect from Us
- Mentorship. You will be paired with a senior data engineer who will guide you through your first projects, review your code, and help you grow into an independent contributor.
- Real ownership. This is not a "shadowing" role. You will own real deliverables from week one - with the support you need to succeed.
- Exposure to the full investment data lifecycle. You will work across conversion, integration, platform, and custom solutions - not just one narrow area.
- Fast‑paced, high‑impact environment. What you build will be used by portfolio managers, traders, and operations teams. You will see your work in production quickly.
- Path to senior. This is a contract role with extension and conversion potential. If you perform well, we will invest in your growth and you will have a clear path to a permanent Senior Data Engineer role over time.
This posting is for an existing vacancy.The expected salary range for this position is $72,000.00 - $108,000.00 per year, prorated based on the term of the contract.
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.
