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

OMERS•Toronto, ON

2d•CA$86,000 - CA$130,000•Remote

About The Position

This is a senior 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 documentation function. We are building a data capability that has to run reliably 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. Conversion of holdings, transactions and reference data from legacy platforms into Aladdin, including reconciliation and break resolution through parallel run. Data Integration. System and vendor integrations into and out of Aladdin across order management, treasury, private markets and market data providers. Data Platform. Build-out of our Snowflake based Integrated Data Platform using a medallion pattern, including data modelling, data quality controls and lineage. Custom Solutions. Design of 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 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 is the single most important attribute here, and you should expect to move between delivery areas as priorities shift.

Requirements

6 or more years in business analysis or data analysis roles supporting investment platform, order management, investment accounting, data platform or data warehouse environments, spanning change initiatives and large transformation programs.
Expert level SQL and strong working knowledge of relational and dimensional data modelling. This will be assessed. You should be able to profile data, write complex joins and aggregations, and investigate data issues without engineering support.
Expert knowledge of requirements elicitation, business process modelling, user story writing, and source to target mapping.
Strong understanding of ETL and ELT design patterns, including incremental loading, change data capture, historisation and data quality controls.
Demonstrated experience at Senior Business Analyst 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.
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.
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.

Nice To Haves

Evidence that you have owned something over its life rather than delivered a series of discrete projects. We will ask you to describe a capability you built, who used it, who ran it afterwards and how it was extended.
Depth in one or more of the following domains: Performance and attribution (Methodology-level knowledge of performance measurement and attribution across fixed income and equity strategies, and of the data required to support them, beyond familiarity with the systems that produce them), Order management and trade lifecycle (Working knowledge of order management workflows across the trade lifecycle, including order creation, execution, allocation, confirmation and settlement, compliance rule frameworks, and the data these processes generate. Experience with Aladdin OMS, Charles River or comparable platforms is an asset.), Investment accounting (Methodology-level knowledge of investment accounting concepts, including the IBOR and ABOR distinction, accruals, amortisation, corporate actions processing, and NAV and book value treatment, and the data structures behind them.)
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.
Snowflake, Azure, dbt, Prefect, Azure DevOps or comparable modern data stack tooling.
Bachelor's degree in related fields such as Computer Science, Engineering, Mathematics, Finance, Accounting or Economics. CBAP or CFA certification is an asset but is not a substitute for demonstrated delivery.
You can hold your own with a portfolio manager, an operations lead and a data engineer in the same conversation, and you write clearly enough that your requirements survive without you in the room.
Experience using generative AI tools and prompt engineering to accelerate analysis and documentation.

Responsibilities

Elicit, document and own business and data requirements using the full analyst toolkit: business requirements documents, user stories and acceptance criteria, business process models (BPMN), data flow diagrams, and source to target mapping specifications.
Perform hands-on data analysis. You will query source and target systems yourself to profile data, investigate reconciliation breaks, validate transformations and prove that what was built is correct.
Facilitate design sessions and workshops with investment, operations, risk, performance and finance stakeholders, and translate between business intent and technical design in both directions.
Own a data capability end to end rather than a queue of requests. You will define the problem, shape the solution with engineers and architects, see it into production and stay accountable for whether it works.
Turn multiple similar-looking requests into one extensible capability. Where stakeholders ask for a specific instrument or report, 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 build.
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.
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.
Operate across both Agile and waterfall delivery models, and keep scope, deliverables and 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 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 behaves.
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.
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.