Principal Data Architect

As Principal Data Architect, you will define the architecture vision across complex client engagements, own key design decisions, and remain accountable for the platforms clients will rely on long-term. You will work directly with senior stakeholders such as CTOs, CDOs, and VPs of Data and Finance, while mentoring engineering teams and setting standards for data architecture across cloud environments, particularly Azure.

Responsibilities:

• Design and own end-to-end data and enterprise solution architecture from concept to delivery across Azure and other cloud platforms.
• Act as the final design authority on data modelling, platform selection, and integration architecture across engagements.
• Design conceptual, logical, and physical data models for advanced analytics and machine learning solutions.
• Drive architecture decisions involving technologies such as BigQuery, Dataflow, Databricks, Cloud Storage, Synapse, and ADLS, with full accountability for outcomes.
• Set and enforce architecture standards, principles, and governance frameworks across delivery teams.
• Shape engagement direction with senior client stakeholders before project scope is finalised.
• Serve as a trusted technical advisor for critical architecture and platform decisions.
• Build strong understanding of client industries, data landscapes, business pressures, and technology stacks.
• Mentor and develop engineering and analytics teams, setting a high bar for technical excellence.
• Contribute to pre-sales activities, proof of concepts, and RFP responses.

Requirements:

• 8+ years of experience in data architecture, with a strong track record of owning architecture decisions end-to-end.
• Demonstrated experience delivering multiple enterprise-grade data and analytics solutions on Azure and GCP in production environments.
• Hands-on experience with Azure Databricks in production at enterprise scale.
• Deep expertise with data warehousing and analytics services such as BigQuery, Cloud Storage, Looker, Azure Synapse, and ADLS Gen2.
• Hands-on experience with data ingestion, processing, and orchestration tools including Dataflow, Pub/Sub, Dataproc, Cloud Composer, Data Fusion, and ADF.
• Strong practical command of data modelling techniques including relational, star, snowflake, and Data Vault across transactional, time-series, and unstructured datasets.
• Experience with Medallion Architecture, Data Mesh, Data Fabric, Data Products, and Data Contracts in real production deployments.
• Strong enterprise data management expertise in data quality, metadata management, data governance, data lineage, data observability, and AgentOps practices for AI systems, including governance, observability, cost control, and evaluation in CI/CD.
• Strong foundation in DataOps, CI/CD, and software development methodologies.
• Hands-on coding experience in Python, SQL, and Spark, along with practical experience using Terraform, Cloud Build, and GitHub Actions.
• Familiarity with AI, GenAI, and agentic AI concepts, including Vertex AI, applied AI architecture, and enterprise agentic use cases.
• Experience with platform migration and modernisation, including assessing and migrating data estates from sources such as MongoDB, PostgreSQL, Snowflake, Redshift, and BigQuery onto modern lakehouse platforms.