Data Architect
Posted
Summary
Data Architect at TMF Group defining the target-state data architecture for its Data Intelligence practice: data modelling, integration patterns (batch, CDC, streaming, APIs) and lakehouse/warehouse platforms on Microsoft Azure, Microsoft Fabric, Databricks and Snowflake, plus data governance, quality, security and technical mentoring.
- Architecture Strategy and Standards
- 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.
- Solution Architecture and Design
- 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.
- Data Modelling
- 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.
- Data Integration and Platform
- 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.
- Data Governance, Quality and Security
- 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.
- Analytics and AI Enablement
- 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.
- Leadership and Collaboration
- 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.