Job Title

Role: Data Engineer (Cloud Data Warehouse | SQL | Medallion Architecture)Location: Toronto, ON (Hybrid 3days onsite, 2days remote)Visa: Any (OWP/PR/Citizen)Duration: 12 months with potential to extendRole SummaryWe are seeking a hands-on Data Engineer with strong experience in enterprise data platforms, Azure Databricks preferred (not mandatory), advanced SQL, and Medallion (Bronze/Silver/Gold) architecture. This role will support Phase 3 data domain and data product design initiative by reverse engineering complex SQL transformation logic and producing detailed, consumable source-to-target mapping and transformation documentation across additional enterprise domains.Key ResponsibilitiesAnalyse and reverse engineer complex SQL scripts and multi-step transformation pipelines (often spanning multiple hops) from source systems through the current data platform.Document detailed source-to-target mappings across Medallion layers (Bronze Silver Gold), including field-level lineage, joins, filters, aggregations, and derivations.Capture transformation rules and business logic embedded in SQL (and/or Spark SQL) and translate them into clear, structured mapping artifacts for downstream engineering teams.Partner with data product, architecture, and domain SMEs to validate mapping assumptions, clarify business definitions, and resolve data ambiguities.Produce high-quality data mapping deliverables (e.g., mapping sheets, rule catalogs, lineage summaries) that are traceable, reviewable, and audit-friendly.Identify data quality checks and reconciliation approaches (e.g., row counts, control totals, null/duplicate checks) to confirm transformations align to intended outcomes.Contribute to reusable documentation patterns/standards to improve consistency across domains (naming conventions, mapping templates, and documentation structure).Support knowledge transfer to build/engineering teams that will implement or operationalize the mapped transformations in Databricks.Required QualificationsStrong hands-on expertise in advanced SQL (complex joins, window functions, CTEs, nested queries, performance considerations) and ability to interpret production-grade transformation logic.Proven experience with data mapping and source-to-target documentation for enterprise-scale platforms, including transformation rules and field-level lineage.Working experience of Lakehouse/Medallion approach, including Bronze/Silver/Gold layering concepts.Experience performing data lineage analysis (end-to-end tracing of fields across transformations and tables).Experience defining/implementing data quality checks and validation strategies aligned to transformations and business rules.Working experience with data contractsStrong documentation skills with attention to detail; ability to produce clear artifacts consumable by multiple teams.The lead resource should have flexibility to travel to Toronto as needed.Preferred QualificationsExperience working with banking / financial services data domains (e.g., customer, accounts, transactions, risk, finance, regulatory reporting).Experience in enterprise data domain and data product design initiatives (data contracts, domain-aligned datasets, standardized definitions).Familiarity with metadata, cataloguing, and governance practices (e.g., data dictionaries, lineage documentation, stewardship inputs).Experience collaborating with geographically distributed teams and stakeholders across business and technology functions.Education & CertificationsBachelors degree in Computer Science, Engineering, Information Systems, or related discipline (or equivalent practical experience).Preferred (not mandatory): Azure/AWS data certifications (e.g., Azure data engineering credentials).Expected DeliverablesField-level source-to-target mapping sheets for Bronze, Silver, and Gold datasets (including data types, nullability assumptions, keys, and standardization rules as available).Transformation rule catalogue capturing joins, filters, aggregations, calculation logic, and derived fields.Data lineage summaries (table-to-table and column-to-column) across multi-hop transformations.Data validation / reconciliation checklist aligned to each mapped flow (counts, control totals, key checks, and exception handling notes).Open items / assumptions log and clarification questions for SMEs and domain owners.Core CompetenciesStrong analytical and problem-solving skills; ability to break down complex SQL logic into understandable business and technical rules.Excellent written and verbal communication, with a focus on producing crisp, unambiguous documentation.Stakeholder management and collaboration skills to work with business SMEs, data architects, and engineering teams.High attention to detail, quality mindset, and comfort working with ambiguity in enterprise environments.Ability to manage deliverables across multiple domains with predictable execution and clear status reporting.