Senior Data Platform / Data Solutions Lead (Capital Markets)

Matrix Global•Toronto, ON

About The Position

We are seeking a highly analytical and strategic professional to join our team as a Data Solutions Lead. This role is ideal for someone who thrives in complex, ambiguous environments and enjoys shaping the design and evolution of large-scale data platforms. You will act as a key thought partner across business and technology teams, driving discussions that define not only what needs to be built, but what should be built.

Requirements

Strong experience working with Capital Markets data (trade, position, lifecycle events)
Exposure to multiple asset classes (e.g., equities, fixed income, derivatives)
Proven background in data modeling, data architecture, or advanced data analysis in large-scale environments
Ability to navigate complex discussions with diverse stakeholder groups
Strong analytical skills to identify root causes and data inconsistencies quickly
Capability to translate complex data problems into clear, actionable solutions
Structured thinking with a data-driven approach to decision-making
Ability to influence stakeholders and drive consensus
Mindset of a trusted advisor rather than a pure execution-oriented resource

Nice To Haves

Apache Kafka
Databricks
Azure Data Lake Storage (ADLS)
Dremio
SQL
JSON-based data structures
Large-scale data processing, reconciliation, and analytics workflows

Responsibilities

Partner with business and technology stakeholders to define end-to-end data solutions, moving beyond requirements gathering into solution design and shaping
Challenge assumptions, identify gaps, and resolve inconsistencies across stakeholder inputs
Facilitate alignment across teams, especially in situations involving conflicting priorities or viewpoints
Perform detailed data profiling, gap analysis, and impact assessments across multiple source systems
Analyze trade, position, and lifecycle event data across various asset classes
Define and implement data mapping standards, business rules, data lineage, and data quality controls
Evaluate and recommend data models and architectural approaches
Collaborate closely with engineering teams to translate business needs into scalable, production-ready solutions
Influence target-state architecture, platform strategy, and operating model design
Drive structured discussions to enable decision-making in complex environments