Data Architect at TMF Group | Jobyssey

Define, document and maintain the target-state data architecture for the Data Intelligence practice, covering ingestion, storage (lake, lakehouse and warehouse), integration, semantic and consumption layers, with a roadmap tied to business priorities.
Set architecture principles, standards and reusable reference patterns (e.g., medallion layering, change data capture, API-based integration, semantic modelling) that engineering teams apply consistently.
Evaluate emerging data and AI technologies, lead proofs of concept and make evidence-based build, buy and platform recommendations.

Translate business and client requirements into end-to-end solution architectures that meet functional needs and non-functional requirements for scalability, performance, availability, security and cost.
Produce and maintain architecture artefacts, including solution designs, data flow and lineage diagrams, integration specifications and architecture decision records.
Lead or take part in design and architecture reviews to make sure solutions follow agreed standards before build and release.

Own conceptual, logical and physical data models across enterprise and domain areas, including dimensional models, normalized models and data vault where appropriate.
Design canonical models for core business entities such as clients, legal entities, funds, investors, accounts and transactions across financial and operational source systems.
Design semantic layers and certified datasets that give BI and self-service analytics users consistent, trusted definitions.

Define integration patterns (batch, CDC, event and streaming, REST APIs) for moving data between business applications, databases and the enterprise data platform.
Guide platform design on Microsoft Azure, Microsoft Fabric, Databricks and Snowflake, including storage and partitioning strategy, performance tuning, workload management and cost optimization.
Work with data engineers on the design of ingestion and transformation pipelines (ETL/ELT), reviewing their approach and building prototypes of complex components when needed.

Work with data governance and business data owners to put ownership, stewardship, metadata management, cataloguing and lineage into practice.
Define the data quality framework, covering profiling standards, data quality rules, monitoring, thresholds and remediation workflows, and see that it is built into delivery pipelines.
Design master and reference data management for key entities to provide a single, reconciled view across systems.
Build security and privacy into the architecture through data classification, role-based access, masking, encryption and retention, in line with GDPR, other applicable regulations and client contractual obligations.

Design data foundations that support advanced analytics, machine learning and generative AI use cases, including feature-ready data, governed access and integration with MLOps practices.
Work with analytics and data science teams so that models and AI solutions are built on trusted, well-documented data.

Provide technical leadership and mentoring to data engineers and BI developers through design guidance, code and model reviews, and knowledge sharing.
Take part in client-facing activities, including requirements workshops, solution presentations and design reviews, and explain technical trade-offs and business benefits in plain language.
Investigate complex data defects and performance issues, identify root causes and recommend improvements to architecture, processes and policies.
Work within agile delivery teams, contributing to backlog refinement, estimation and delivery planning.

Data Architect

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