Data Architect at Kuda

Role Overview

• We are looking for a Data Architect to oversee the design, governance, and optimisation of our organisation’s data architecture. This role will blend strategic vision with practical expertise in modern data engineering.
• The goal is to ensure that our data ecosystem supports advanced analytics, artificial intelligence, and real-time decision-making, all while adhering to industry best practices, such as the principles outlined in DAMA-DMBOK.

Roles and responsibilities

• Define and maintain the enterprise data architecture blueprint aligned with business strategy, DAMA-DMBOK principles, and modern data platform best practices.
• Collaborate with enterprise architects to ensure data architecture is fully integrated into the overall IT and business architecture.
• Lead the adoption of data modelling standards (conceptual, logical, and physical), including dimensional modelling and data vault methodologies.
• Oversee Master Data Management (MDM) strategy, implementation, and governance across domains such as Customer, Product, and financial data.
• Ensure architectural compliance with data governance policies, security frameworks, and regulatory requirements (GDPR, NDPR, ISO 27001).
• Design scalable, secure, and high-performance data solutions across multi-cloud and hybrid environments (Azure SQL, Google BigQuery, on-prem SQL Server).
• Define integration patterns for batch and streaming data ingestion using tools such as Airbyte, Azure Data Factory, and FiveTran.
• Oversee transformation layer design using Dataform and dbt. Cloud, ensuring version control, testing, and deployment best practices.
• Recommend and implement data storage strategies, including Medallion Architecture, Data Mesh, and Lakehouse patterns.
• Collaborate with infrastructure teams to provision infrastructure-as-code (Terraform or Pulumi) for data workloads.
• Partner with Data Governance teams to establish and enforce metadata management, data lineage, and business glossary standards.
• Define data quality frameworks and embed automated validation and anomaly detection in ingestion and transformation pipelines.
• Champion data stewardship practices across the business.
• Act as the primary authority on data architecture, advising C-level executives, engineering teams, and business stakeholders.
• Mentor senior data engineers, analysts, and modellers on best practices in architecture, modelling, and governance.
• Facilitate cross-functional workshops to align business requirements with architectural design.
• Stay ahead of industry trends, recommending emerging tools and patterns for modern data platforms.
• Continuously optimise cost, performance, and scalability of data platforms.
• Lead proof-of-concept initiatives for innovative data solutions, including AI- driven data management tools.

Requirements

• 10+ years in data-focused roles, with 3+ years in data architecture or enterprise architecture positions.
• A Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering, or a related field is required.
• DAMA-DMBOK knowledge required; CDMP (Certified Data Management Professional) or equivalent certification preferred.
• TOGAF, Zachman, or Certified Enterprise Architect certification is an advantage.
• Advanced expertise in data modelling (conceptual, logical, physical) and MDM frameworks.
• Strong SQL skills with proven experience in Azure SQL, Google BigQuery, and Microsoft SQL Server.

Hands-on experience with:

• Ingestion: Airbyte, Azure Data Factory, Fivetran
• Transformation: Dataform, dbt Cloud
• Processing: Apache Beam/Dataflow, Spark
• Streaming: Kafka, Pub/Sub, Spark Streaming, or Flink
• Strong knowledge of modern data architectures (Medallion, Data Mesh, Lakehouse).
• Familiarity with orchestration tools (Airflow, Cloud Composer, Workflows).
• Proficiency in infrastructure-as-code (Terraform, Pulumi).
• Understanding of data security and compliance best practices.

Preferred Skills

• Experience in cloud cost optimisation for data workloads.
• Familiarity with data catalogue tools such as Collibra, Alation, or Data Catalogue.
• Experience in BI integration (Looker, Power BI, Data Studio).
• Exposure to machine learning data pipelines (Vertex AI, MLflow).

Data Stack

• Databases: Microsoft SQL Server, Azure SQL, Google BigQuery
• ngestion: Airbyte, Fivetran, Azure Data Factory
• Processing & Modelling: Dataform, dbt Cloud, Apache Beam/Dataflow, Spark
• Visualisation: Looker, Data Studio
• Cloud: Azure, Google Cloud Platform
• Orchestration: Airflow, Cloud Composer, Workflows
• IaC: Terraform, Pulumi