Data Solutions Architect

Do you want to boost your career and collaborate with expert, talented colleagues to solve and deliver against our clients' most important challenges? We are growing and are looking for people to join our team. You'll be part of an entrepreneurial, high-growth environment of 300.000 employees. Our dynamic organization allows you to work across functional business pillars, contributing your ideas, experiences, diverse thinking, and a strong mindset. Are you ready?

About your role

Enterprise AI is forcing organisations to rethink their data estates. Traditional reporting platforms are no longer enough for GenAI and AI-driven decision-making. Clients need data that is trusted, governed and consumable by both people and intelligent systems.

As a Databricks Architect, you will help clients transform fragmented data into AI-ready lakehouse platforms. You’ll advise on architecture across Databricks and cloud ecosystems, covering data products, governance, metadata, lineage, semantic layers and GenAI data patterns.

This is a client-facing consulting role. You will solve ambiguous problems, shape architecture decisions, and communicate clearly with both technical teams and senior stakeholders.

Working in cross-functional teams, you’ll deliver target architectures, platform assessments, data product designs, governance models and implementation roadmaps.

We’re hiring across levels. Junior roles focus on delivery and platform expertise; senior roles require enterprise thinking, stakeholder influence and leadership.

We’re looking for strong data architects with depth in areas such as Databricks/lakehouse architecture, governance, semantic modelling, data products, metadata or GenAI (e.g. RAG).

Requirements

• Design and deliver modern data lakehouse solutions using Databricks, Delta Lake, Unity Catalog, PySpark, Spark SQL, and cloud data services.
• Define end-to-end architecture covering ingestion, transformation, data modelling, governance, security, orchestration, monitoring, and consumption.
• Build reusable patterns for batch, streaming, and near-real-time data pipelines.
• Implement strong data governance using Unity Catalog, including access control, lineage, cataloguing, and data ownership.
• Work with cloud platforms such as Azure, AWS, or GCP and integrate Databricks with tools like ADF, Power BI, Purview, Snowflake, dbt, Kafka, and APIs.
• Guide teams on performance tuning, cost optimisation, cluster strategy, job scheduling, and production support.
• Drive DevOps/DataOps practices using CI/CD, Git, Terraform, Databricks workflows, and automated deployment.