Data and AI – Data Architect or Engineer

Overview

In this client-facing role within Wavestone’s Data & AI Practice, you will design and deliver modern data solutions for regulated industries, enabling advanced analytics and AI. You’ll lead data architecture and platform initiatives, govern data quality and lineage, and drive value across large transformation programs. You collaborate with business and tech stakeholders to translate requirements into scalable cloud data solutions. This is an opportunity to shape data ecosystems for clients while growing your expertise in data architecture and AI enablement.

Pay / Benefits

• Competitive salary and bonus scheme
• Income protection insurance
• 5% company pension
• Private health and dental cover
• Life insurance
• Company share scheme

Responsibilities

• Lead and deliver complex data transformation engagements, translating requirements into scalable architectures
• Advise on enterprise data strategies, target operating models, and governance frameworks
• Design and govern modern data platforms (lakehouse, data warehouse, data mesh) and integration architectures
• Develop scalable data pipelines and cloud-native solutions enabling trusted analytics and AI use cases
• Support business development, shaping proposals and identifying opportunities to expand data & AI footprint
• Contribute to thought leadership, methodologies, accelerators, and market-facing insights
• Coach and mentor colleagues, contributing to internal capability building and best practices
• Stay updated on data architecture, cloud tech, analytics, and AI enablement
• Maintain and improve data governance, metadata, lineage, and data quality practices
• Provide architectural leadership across delivery teams to ensure end-to-end value

Key requirements

• Significant experience in consulting or client-facing roles delivering data engineering and architecture solutions
• Strong data engineering principles: pipelines, ETL/ELT, ingestion, orchestration, integration
• Experience designing or governing lakehouse, data warehouse, data mesh, or data fabric architectures
• Hands-on with cloud data ecosystems (Azure, AWS, GCP) and platforms like Databricks and Snowflake
• Desirable: SQL and/or Python, dbt, Git, CI/CD, MLOps familiarity
• Understanding of BI/visualisation concepts and downstream analytics needs

• Cause-effective communication of complex concepts to technical and non-technical audiences
• Collaborative, client-facing mindset with ability to influence stakeholders
• Thought leadership and willingness to mentor others
• Azure (Data Factory, Synapse, Fabric, ADLS, Databricks)
• AWS (Glue, Redshift, EMR)
• GCP (BigQuery, Dataflow)

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