Title: Data Engineer Location: Toronto, ON (Onsite) Duration: + Months Daily Responsibilities: This role bridges data modeling, data product strategy, and business operations to establish a unified, asset-agnostic data ecosystem.

You will own the end-to-end data product lifecyclefrom requirements definition through deploymentwhile collaborating and driving partnerships with middle office and technology stakeholders to translate operational complexity into scalable, maintainable data solutions.

Contribute to the execution of all phases of the data requirements process, including planning, data elicitation and extraction, analysis, documentation, and support.

Establish best practices for data modeling and ontology across the team, and contribute to building trade and transaction data capabilities.

Partner with stakeholders to understand data sources, business event needs, requirements, data lineage, technical specifications, and key success metrics, ensuring alignment with project objectives.

Define comprehensive functional and non-functional requirements for technology, architecture, tooling, and data products supporting the modernization program.

Work with architecture teams to provide requirements that ensure data solutions are real-time and asset-agnostic and support current and future product expansion.

Validate and enhance the exception schema and data model for the Common Exception Manager platform, enabling unified detection and resolution across all asset classes.

This includes defining exception taxonomies, root cause classifications, and replay functions; building data validation rules that feed the UI; conducting end-to-end testing across trade workflows; and establishing rollout strategies to minimize operational disruption.

Analyze and document trade lifecycle requirements and establish the data models that support real-time exception and transaction processing.





Contribute to data taxonomy and ontology frameworks that create common language required for the exception layer.

Define system and field level data sourcing requirements including system/database table, columns, etc. creating detailed field-level mapping documentation and lineage.

Create and maintain physical data dictionaries and process documentation.

Conduct data profiling to understand data quality issues, patterns, and anomalies across source systems.

Define and prioritize data quality rules aligned with business requirements, translate rules into executable validation logic, implement into UIs, and lead testing efforts to ensure rules function correctly under production conditions.

Using the latest technologies available, define and implement strategic mechanisms for analyzing data quality and identifying data anomalies, while improving how we publish data trends and dashboards.

Partner with data stewards and custodians/technology-owners, to diagnose and remediate data gaps across data flows/lineage.

Conduct advanced data analysis, identify trends, and provide actionable insights to drive evidence-based decision-making.

Identify opportunities to optimize data processes, automate workflows, and improve problem-solving methodologies to better support operational and strategic objectives.

Must have skills: Experience establishing a centralized exception data layer, extensive experience with logical / physical relational data modeling, data profiling, pattern recognition, advanced data analysis, trade and transaction data expertise, data taxonomy and ontology Bachelors Degree Mindlance is an equal prospect employer.

We are committed to inclusive, equitable, barrier-free recruitment and selection processes, and work environment in accordance with the Accessibility for Ontarians with Disabilities Act (AODA).

We will be happy to work with applicants requesting accommodation at any stage of the hiring process.

We use AI in our Hiring processes

📌 IT - Data Engineer - Expert (Toronto)
🏢 Mindlance
📍 Toronto