Description

Key Responsibilities

• Understand existing transformations: Analyse and interpret current-state SQL transformation logic (including multi-hop transformations) and collaborate with data engineers to validate business rules and assumptions.
• Review and leverage mapping outputs: Review source-to-target mapping sheets produced by engineering teams and translate mapping logic into robust domain and entity models.
• Create conceptual & logical models: Develop conceptual and logical data models for assigned domains, including entities, attributes, relationships, keys, cardinalities, and normalization patterns as required.
• Define data domains & sub-domains: Help structure domain boundaries, sub-domain decomposition, and shared/lookup concepts; align to enterprise data architecture standards and governance expectations.
• Design data products: Define data products per domain, including product purpose, consumers, SLAs/SLOs (where applicable), interfaces, and versioning considerations.
• Define business definitions & metadata: Document business-friendly definitions, attribute semantics, reference data, master data concepts, and metadata required for discoverability and reuse.
• Establish data contracts: Define and maintain data contracts between producers and consumers (schemas, constraints, expectations, change management, and compatibility) across Bronze/Silver/Gold layers.
• Align models to Medallion layers: Ensure modelling decisions map cleanly to Bronze (raw ingestion), Silver (conformed/cleansed), and Gold (curated/consumption) structures.
• Lineage & traceability: Capture lineage at a logical level (source → Bronze → Silver → Gold) and ensure traceability from business requirement → mapping → model → downstream consumption.
• Stakeholder collaboration: Facilitate working sessions with business SMEs, architects, and engineering to validate models, resolve ambiguities, and drive sign-off.
• Documentation & model governance: Produce high-quality modelling documentation and adhere to review/sign-off processes; maintain model change logs and support governance checkpoints.

Key Deliverables

• Domain model package for each assigned domain (conceptual + logical models).
• Entity/attribute catalogue with business definitions, data types, constraints, and stewardship notes.
• Data product definitions per domain (scope, consumers, access patterns, and success criteria).
• Data contracts for key datasets/tables across Bronze/Silver/Gold layers.
• Source to Target (STM) mapping documentation
• Lineage documentation linking sources to curated outputs and highlighting major transformation checkpoints.
• Model review materials and sign-off artifacts (workshop outputs, decisions, and change log).

Required Qualifications

• Strong hands-on experience in conceptual and logical data modelling for enterprise-scale data platforms.
• Proven experience with data domain modelling and data product thinking (domain boundaries, ownership, consumption, and reuse).
• Experience defining entities, attributes, relationships, and producing clear model documentation consumable by engineers and business stakeholders.
• Strong SQL analysis skills with the ability to understand complex transformation logic and translate it into model intent.
• Working knowledge of enterprise data architecture concepts (conformed dimensions, canonical models, reference/master data, and governance).
• Experience with metadata definition and supporting data lineage traceability.
• Excellent written and verbal communication with strong stakeholder facilitation and documentation skills.

Preferred Qualifications

• Experience in banking/financial services data (e.g., customer, accounts, transactions, risk, treasury, AML, payments) and associated controls/terminology.
• Exposure to Azure Databricks and Lakehouse/Medallion concepts; familiarity with Delta Lake patterns is a plus.
• Familiarity with data catalogue/governance tooling (e.g., Collibra, Alation, Purview) and model repositories is a plus.
• Experience working in consulting/engagement settings with fast iteration cycles and multiple parallel domains.

Core Skills & Competencies

• Modelling: Conceptual modelling, logical modelling, normalization, entity lifecycle, slowly changing dimensions (conceptual understanding), conformed data modelling, and shared/common entities.
• Domain & product: Domain-driven data design, data product definition, data contract design, and interface/schema versioning basics.
• Data platform patterns: Medallion architecture alignment (Bronze/Silver/Gold), lakehouse concepts, and curation for analytics/consumption.
• Analysis: Advanced SQL reading/analysis, mapping validation, and transformation rule documentation.
• Metadata & lineage: Business glossary, metadata standards, lineage documentation, and traceability.
• Communication: Workshop facilitation, stakeholder management, and crisp documentation/presentations

Key Skills

Education

Bachelor's degree