Data Business Analyst Toronto

• Role / Skill - Data BA
• Location: Toronto Day 1 Onsite - with 3 days at office.

JD:

• 1. Capital Markets Data Modeling
  * Design and maintain physical and logical data models for Capital Markets domains including trade execution portfolio positions market data (ticks/pricing) and risk metrics.
  Translate complex financial hierarchies (e.g. fund-of-funds multi-asset class structures) into optimized Databricksstructures.
  Ensure data models support temporal requirements such as Point-in-Time (PIT) analysis and As-Of financial reporting.
  2. Advanced STM (Source-to-Target Mapping) Creation
  Lead the creation of comprehensive STM documents detailing the journey from legacy financial systems and external providers (e.g. Bloomberg Reuters Aladdin) to ADLS Gen2.
  Define complex transformation logic business rules and data enrichment steps within the STM to guide Data Engineering squads.
  Map data lineage to ensure full traceability for regulatory compliance.
  3. Databricks & ADLS Performance Engineering
  Design Delta Lake structures that prioritize Shuffle-free joins for massive capital markets datasets.
  Implement optimized partitioning and Z-Ordering strategies specifically for time-series financial data to enable high-speed analytics.
  Utilize Unity Catalog to govern data access and maintain a centralized metadata repository for the GWAM ecosystem.
  

Required Skills & Qualifications
Industry Expertise: 5 years of experience in Capital Markets or Wealth Management with a deep understanding of financial instruments and trade lifecycles.
Technical Modeling: 7 years of experience in data modeling with a mastery of Azure Databricks and ADLS Gen2.
STM Mastery: Proven track record of creating highly detailed Source-to-Target Mappings for complex data migration or integration projects.
Data Engine Proficiency: Expert-level Spark SQL and PySpark. Ability to optimize data structures for the Spark Catalyst Optimizer.
Storage Formats: Expertise in Delta Lake (ACID transactions Time Travel) and Parquet optimization.
Governance: Hands-on experience implementing data governance and security via Unity Catalog.

Technical Stack
Compute: Azure Databricks (Jobs SQL Warehouses).
Storage: ADLS Gen2 (Delta/Parquet).
Governance: Unity Catalog.
Analysis Tools: SQL Python Excel (for data profiling).
Documentation: Confluence/Visio for STM and ERDs.