Databricks Champion Architect

In this role you will define and govern modern lakehouse architectures on Databricks, guiding cross-functional teams and setting standards and best practices. You will own end-to-end Databricks solution design across ingestion, processing, governance, serving, and operations, driving platform enablement and production readiness. You will shape reference designs, data contracts, and CI/CD patterns, while upskilling delivery squads and contributing to pre-sales. This is a high-impact, architecture-led role that combines hands-on leadership with strategy to accelerate data delivery at scale.

• flexible work options
• learning and development opportunities
• range of tailored benefits
• emphasis on wellbeing and diversity
• equal opportunity employer
• Disability Confident Committed Employer

• Own end-to-end Databricks solution architecture (ingestion, transformation, governance, serving, operations)
• Define and enforce Medallion Architecture standards (Bronze/Silver/Gold) and reference designs
• Architect ingestion approaches (AutoLoader, Lakeflow Connect, batch/streaming/CDC)
• Establish platform governance using Unity Catalog (namespace strategy, access, lineage, data sharing)
• Define Declarative Pipelines with Expectations (DLT): data quality, validation, observability
• Champion production-ready engineering standards (CI/CD, environments, monitoring, runbooks)
• Drive stakeholder alignment with security, enterprise architecture, governance, platform teams, and delivery squads
• Enable and upskill teams with playbooks, ADRs, reviews, and coaching
• Contribute to pre-sales and roadmap with high-level designs, estimation, and risk management

• 2+ years of hands-on Databricks experience with modern features and production delivery
• Databricks Certification (two of: Data Engineer Associate/Professional, Machine Learning Associate/Professional, Generative AI Engineer Associate)
• Medallion Architecture design ownership (Bronze/Silver/Gold, data contracts, lifecycle, serving patterns)
• Unity Catalog architecture experience (governance model, lineage, secure access)
• Declarative Pipelines with Expectations (DLT) architectural understanding and practical implementation
• Ingestion architecture & delivery experience (AutoLoader and/or Lakeflow Connect, schema evolution, incremental loads)
• Knowledge of latest Databricks capabilities and platform evolution

• Influence and standardise across teams
• Cross-functional collaboration
• Technical coaching and mentoring
• Delta Lake expertise (MERGE/CDC, partitioning, OPTIMIZE, VACUUM)
• Production architecture practices (environment separation, secrets, Databricks Workflows)
• Cloud platform integration (Azure/AWS/GCP)