Data Architect

Project Description: We are seeking a Data Architect to join our AI project team. In this role, you will design and implement the data architecture needed to support machine learning and AI solutions, including defining data models, storage patterns, and governance frameworks. You will ensure that data from various sources is well-organised, accessible, and AI-ready, working closely with data engineers and ML engineers to build robust data pipelines and maintain high data quality for analytics and model development.

Responsibilities

• :Data Modelling & Schema Design: Develop and maintain data models (conceptual, logical, and physical) that define how data is stored and related. This includes designing relational schemas, graph data models for knowledge graphs, and time-series data structures as needed, ensuring they accurately represent business entities and relationships. You will continually refine these models to meet AI use cases and evolving business requirements
• .Data Storage Architecture: Define and implement data storage and management patterns that optimise data retrieval and analytics performance. This involves selecting or designing appropriate storage solutions (e.g. relational databases, NoSQL/graph databases, data warehouses, data lakes) and structuring them for scalability and fast access to large datasets used in AI projects. Ensure the architecture can handle structured and unstructured data and is cloud-ready for elasticity
• .Data Pipelines & Integration: Build and oversee robust data pipelines (ETL/ELT processes) to integrate data from multiple sources into centralised platforms. You will design workflows to collect, transform, and load data into analytics repositories or feature stores, guaranteeing that AI models have consistent, well-prepared data to work with. This includes setting up stream processing for real-time data when required and automating pipeline orchestration for efficiency
• .Data Governance & Quality: Establish and enforce data governance policies and standards. This means defining practices for data quality, data cleaning, and master data management, as well as setting security and privacy controls to protect sensitive information. You will ensure compliance with relevant data regulations and implement data security measures (e.g. access controls, encryption) and validation rules so that the data used in AI is trustworthy and compliant
• .Metadata Management & Lineage: Implement frameworks for data metadata management and lineage tracking. This includes maintaining data catalogues or dictionaries that describe data meaning (possibly leveraging ontologies), and tools or processes to trace how data flows through pipelines and transformations. By providing transparency into data origins and transformations, you support model interpretability and enable troubleshooting of data issues, which is critical in AI development
• .Collaboration with Engineering Teams: Work closely with data engineers, ML engineers, and data scientists to ensure the data architecture meets their needs. You will collaborate on designing data interfaces (e.g. APIs or query endpoints) and assist in shaping how data is used for features in machine learning. This role requires translating requirements between data teams and ML teams, and jointly resolving issues to streamline the path from raw data to AI insights
• .Performance Optimisation & Scaling: Monitor the performance and scalability of the data infrastructure, and tune it as the AI project grows. Optimise database queries, indexing, and storage layouts for faster model training and inference data access. Plan for scale by leveraging cloud capabilities (compute, storage) and manage costs effectively, adjusting architectures (partitioning, caching, etc.) to maintain efficient, cost-effective operations as data volumes increase. You may also evaluate new technologies (e.g. distributed computing frameworks or new databases) and incorporate them to continually improve the architecture

. Mandatory Skills Descriptio

• n:Education: Bachelor's degree in Computer Science, Information Systems, or a related field (or equivalent professional experience). An advanced degree is a plus but not require
• d.Experience: Approximately 3-5 years of experience in data architecture, data engineering, or a related data management role. A proven track record in designing data solutions and managing data schemas is expecte
• d.Data Modelling & Databases: Strong proficiency in data modelling and database design. You should be comfortable creating ER diagrams and defining relational schema, as well as working with NoSQL databases (e.g. document or graph databases). Practical experience with SQL and at least one relational database is required, as well as deep knowledge of other data store types (especially graph databases) is highly neede
• d.Data Pipeline Development: Hands-on experience developing data pipelines and integration workflows. This includes proficiency in ETL/ELT tools or frameworks (or custom scripting with Python/SQL) to gather and transform data. You should understand how to optimise data flow and have experience with batch processing; experience with real-time streaming data (e.g. using Kafka or equivalent) is a plu
• s.Ontologies & Knowledge Graphs: Exposure to semantic data modelling, ontologies, or knowledge graph construction. Experience in structuring data with ontologies (e.g. using RDF/OWL standards) or implementing a knowledge graph to link datasets can be very beneficial, since it helps in creating a unified data vocabulary and enriches the context for AI model
• s.Cloud Data Platforms: Experience working with cloud-based data platforms or big data technologies. While our approach is cloud-agnostic, you should be familiar with concepts like data lakes, data warehouses, and distributed computing in a cloud environment (e.g. using AWS, Azure, or GCP services). The ability to design solutions that leverage cloud scalability and tools for storage and processing is importan
• t.Data Governance & Security: Solid understanding of data governance principles and best practices. You should be knowledgeable about data privacy regulations and data protection techniques, ensuring compliance in how data is stored and used. Experience implementing data quality checks, defining data standards, and using or setting up metadata management tools will be usefu
• l.Communication & Teamwork: Excellent communication skills with the ability to collaborate in cross-functional teams. You should be able to translate complex data architecture concepts into clear terms for project managers or stakeholders, and work closely with engineering teams to guide implementation. Problem-solving aptitude and a willingness to mentor junior data team members are also important in our collaborative environmen

t. Nice-to-Have Skills Descripti

• on:AI/ML Project Involvement: Experience working on projects that involve AI or machine learning, where you partnered with data scientists or ML engineers. For example, having supported an ML model deployment by providing well-structured data and ensuring data reliability. This background will help you anticipate the needs of AI initiatives and design data architectures that facilitate model training and inferen
• ce.Data Governance Tools: Familiarity with data governance or data cataloguing tools (such as Collibra, Alation, or Apache Atlas) and lineage-tracking systems. Hands-on experience setting up or maintaining a data catalogue, documenting data definitions, or automating data lineage capture is a strong plus, as it shows ability to operationalise governance and transparency in data ecosyste
• ms.Modern Data Architecture Patterns: Experience with modern data architecture concepts and patterns. This could include implementing or working with data lakehouse architectures (combining data lake flexibility with data warehouse performance), data mesh principles (decentralising data ownership to domain teams), or event-driven/streaming architectures. Familiarity with these approaches demonstrates adaptability and knowledge of cutting-edge solutions for handling complex data workflo
• ws.Certifications: Relevant industry certifications are advantageous. Certifications such as AWS/Azure/GCP data engineering certifications, Certified Data Management Professional (CDMP), or other credentials in data architecture and cloud services show validated expertise and a commitment to staying current with technology developments. While not mandatory, they could strengthen your candida cy.