Data Architect needed 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