Principal Data Architect - Databricks & AI
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What you'll do
- Define and own the target data architecture, standards, reference patterns and technical roadmap for a strategic enterprise data capability.
- Design a modern Databricks lakehouse foundation that enables secure, scalable use of data across a complex product landscape.
- Establish scalable medallion patterns across Bronze, Silver and Gold layers, including ingestion, transformation, storage, serving and consumption.
- Design canonical, dimensional and semantic data models that enable consistent exchange, interoperability and reuse across product domains.
- Architect secure data integration using batch, streaming, APIs, change data capture and event-driven patterns.
- Design privacy-preserving data capabilities, including data masking, anonymisation or equivalent controls for customer data used in AI and analytical workloads.
- Design AI-ready data capabilities, including governed model-data pipelines, vector search and retrieval-augmented generation patterns where relevant.
- Create prototypes and proof-of-concepts, review technical designs and code, guide performance optimisation, and help resolve complex technical issues.
- Build production-ready solutions with strong governance, lineage, quality, observability, security, reliability and cost control.
- Partner with product, engineering, AI, security, platform and business leaders to translate customer and product needs into architecture and delivery plans.
- Mentor data engineers and architects and promote reusable platform capabilities and engineering standards.
What they're looking for
- Significant experience as a Data Architect, Lead Data Architect, Principal Data Engineer or comparable senior technical leader.
- Advanced, hands-on Databricks experience in production environments, including lakehouse architecture, Delta Lake and relevant governance, workflow, SQL and optimisation capabilities.
- Proven ownership of modern data platforms or data products from architecture through production delivery and operation.
- Strong experience in data privacy and security, including data masking, anonymisation, tokenisation or comparable privacy-preserving patterns for sensitive or customer data.
- Strong data-modelling expertise across conceptual, logical, physical, dimensional and canonical models.
- Experience with batch and real-time integration, including APIs, CDC, streaming or event-driven pipelines.
- A strong data-engineering or software-development foundation using Python, SQL, Scala, Java and/or Spark.
- Deep experience with Azure data services. AWS experience is advantageous as the platform expands.
- Experience with governance, cataloguing, lineage, quality, metadata, access control and secure multi-tenant or customer-data environments.
- Credibility with hands-on engineers and senior stakeholders, with strong judgement across speed, scale, governance, cost and usability.
Nice to have
- Production AI/ML or generative-AI data architecture, including RAG, embeddings, vector search or model-data pipelines.
- Knowledge graphs, ontologies, RDF, linked data or advanced semantic technologies.
- Databricks or cloud architecture/ data-engineering certification.
- Tools such as dbt, Kafka, Airflow, Terraform, Power BI, Tableau, Collibra or Microsoft Purview.
- Architecture across multiple products, domains or business units in B2B software, information services, consulting or regulated environments.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
The opportunity
We are investing in the next generation of our data and technology capabilities to create more connected, intelligent experiences for customers. Data is central to this ambition. This is an opportunity to define the architecture, standards and delivery patterns for a modern data platform that will enable trusted data use across products and support advanced analytics and AI.
The Principal Data Architect will be the senior technical authority for data architecture within this strategic transformation. The role is architecture-led while remaining practically engaged through prototyping, proof-of-concepts, technical reviews and the resolution of complex engineering challenges. As the capability grows, the remit may expand to include leadership of a small team.
What you will do
Define and own the target data architecture, standards, reference patterns and technical roadmap for a strategic enterprise data capability.
Design a modern Databricks lakehouse foundation that enables secure, scalable use of data across a complex product landscape.
Establish scalable medallion patterns across Bronze, Silver and Gold layers, including ingestion, transformation, storage, serving and consumption.
Design canonical, dimensional and semantic data models that enable consistent exchange, interoperability and reuse across product domains.
Architect secure data integration using batch, streaming, APIs, change data capture and event-driven patterns.
Design privacy-preserving data capabilities, including data masking, anonymisation or equivalent controls for customer data used in AI and analytical workloads.
Design AI-ready data capabilities, including governed model-data pipelines, vector search and retrieval-augmented generation patterns where relevant.
Create prototypes and proof-of-concepts, review technical designs and code, guide performance optimisation, and help resolve complex technical issues.
Build production-ready solutions with strong governance, lineage, quality, observability, security, reliability and cost control.
Partner with product, engineering, AI, security, platform and business leaders to translate customer and product needs into architecture and delivery plans.
Mentor data engineers and architects and promote reusable platform capabilities and engineering standards.
What you bring
Significant experience as a Data Architect, Lead Data Architect, Principal Data Engineer or comparable senior technical leader.
Advanced, hands-on Databricks experience in production environments, including lakehouse architecture, Delta Lake and relevant governance, workflow, SQL and optimisation capabilities.
Proven ownership of modern data platforms or data products from architecture through production delivery and operation.
Strong experience in data privacy and security, including data masking, anonymisation, tokenisation or comparable privacy-preserving patterns for sensitive or customer data.
Strong data-modelling expertise across conceptual, logical, physical, dimensional and canonical models.
Experience with batch and real-time integration, including APIs, CDC, streaming or event-driven pipelines.
A strong data-engineering or software-development foundation using Python, SQL, Scala, Java and/or Spark.
Deep experience with Azure data services. AWS experience is advantageous as the platform expands.
Experience with governance, cataloguing, lineage, quality, metadata, access control and secure multi-tenant or customer-data environments.
Credibility with hands-on engineers and senior stakeholders, with strong judgement across speed, scale, governance, cost and usability.
Valuable additional experience
Production AI/ML or generative-AI data architecture, including RAG, embeddings, vector search or model-data pipelines.
Knowledge graphs, ontologies, RDF, linked data or advanced semantic technologies.
Databricks or cloud architecture/data-engineering certification.
Tools such as dbt, Kafka, Airflow, Terraform, Power BI, Tableau, Collibra or Microsoft Purview.
Architecture across multiple products, domains or business units in B2B software, information services, consulting or regulated environments.
Why join
This is a rare opportunity to shape a strategically important data foundation from the beginning. You will have the mandate to make high-impact architectural decisions, work on complex technical challenges and influence how trusted data powers better customer experiences, advanced analytics and AI. The role offers meaningful visibility, broad collaboration and the chance to create capabilities that will scale across an evolving product landscape.
Our Interview Practices
To maintain a fair and genuine hiring process, we kindly ask that all candidates participate in interviews without the assistance of AI tools or external prompts. Our interview process is designed to assess your individual skills, experiences, and communication style. We value authenticity and want to ensure we’re getting to know you—not a digital assistant. To help maintain this integrity, we ask to remove virtual backgrounds and include in-person interviews in our hiring process. Please note that use of AI-generated responses or third-party support during interviews will be grounds for disqualification from the recruitment process.
Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.
Company
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