Principal Data Architect

As Principal/ Senior Architect at Aays, you write the brief. You are the final word on architecture design across our most complex client engagements — defining the vision, owning the decisions, and standing accountable for the platforms our clients will build on for the next decade. You will work directly with senior client stakeholders — CTOs, CDOs, VPs of Data and Finance — shaping the architecture strategy. You will mentor the engineering team and set the standard for how Aays approaches data architecture across cloud environments, particularly Azure.

The candidate will have responsibilities across the following functions:

Architecture Leadership:

• Design and own end-to-end data/enterprise solution architecture from concept to delivery on Azure and other clouds.
• Be the final design authority on data modelling, platform selection, and integration architecture across client engagements.
• Design conceptual, logical, and physical data models for advanced analytics and ML-driven solutions.
• Drive architecture decisions — BigQuery, Dataflow, Databricks, Cloud Storage, Synapse, ADLS — with full accountability for the outcome.
• Set and enforce architecture standards, principles, and governance frameworks across the delivery team.

Client and Stakeholder Engagement:

• Sit in the room when the problem is being defined — shaping the brief with the client's CTO, CDO, or VP of Data before the engagement is scoped.
• Serve as the trusted technical advisor senior client stakeholders turn to when something critical needs an answer.
• Build deep fluency in clients' industries, data landscapes, and competitive pressures, alongside their technology stacks.

Team and Practice Development:

• Mentor and develop the engineering and analytics teams — setting the bar for technical excellence across Aays' delivery practice.
• Contribute to pre-sales engagements, proofs-of-concept, and RFP responses — your credibility is part of what Aays brings to the table.

Requirements:

• 8+ years of experience in data architecture, with a clear 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 — BigQuery, Cloud Storage, Looker, Azure Synapse, ADLS Gen-2.
• Hands-on experience with data ingestion, processing, and orchestration — Dataflow, Pub/Sub, Dataproc, Cloud Composer, Data Fusion, ADF.
• Strong practical command of data modelling techniques — Relational, Star, Snowflake, 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.
• Enterprise data management depth — Data Quality, Metadata Management, Data Governance, Data Lineage, Data Observability, and AgentOps practices (governance, observability, cost control, evals in CI/CD) for AI systems • Strong foundation in DataOps, CI/CD, and software development methodologies.
• Hands-on coding experience in Python, SQL, and Spark; practical experience with Terraform, Cloud Build, and GitHub Actions.
• Familiarity with AI, GenAI, and Agentic AI concepts — Vertex AI, applied AI architecture, and agentic operational use cases in enterprise contexts.
• Experience with platform migration and modernisation — assessing and migrating data estates from sources such as MongoDB, PostgreSQL, Snowflake, Redshift, and BigQuery onto modern lakehouse platforms.