Solution Engineer - Data

Job description

Working alongside account teams, specialists, and partners, you will engage customers throughout their transformation journey, from initial strategy discussions through to technical validation and adoption. Lead strategic customer engagements by helping organizations modernize their data, analytics, and AI platforms to achieve business outcomes. Serve as a trusted technical advisor, translating complex business challenges into scalable cloud, data, and AI solutions.

Design and deliver solution demonstrations, workshops, architecture reviews, and proof-of-concept engagements that support customer decision-making. Partner with sales, customer success, engineering, and partner teams to develop technical strategies that accelerate customer transformation and drive business growth. Guide customers in adopting modern data platforms, analytics capabilities, governance practices, and AI-enabled experiences.

Build strong relationships with technical and business stakeholders, influencing architecture and technology decisions throughout the customer lifecycle. Contribute to competitive strategies, technical readiness, and solution innovation by sharing market insights, customer feedback, and best practices. Mentor peers, contribute to technical communities, and help build a culture of continuous learning, inclusion, and customer obsession.

Bachelor's degree in Computer Science, Engineering, Information Technology, Data Science, or a related discipline or equivalent practical experience. Experience in technical pre-sales, solution architecture, consulting, data engineering, database administration, analytics, or cloud technology roles. Experience designing or implementing enterprise-scale data, database, and analytics solutions. Experience engaging with both technical and business stakeholders in customer-facing environments.

Experience with operational and transactional databases - including SQL Server, Azure SQL (Database & Managed Instance), Azure Database for PostgreSQL, and Cosmos DB - alongside modern analytics platforms. Hands-on database migration and modernisation experience, including estate assessment, heterogeneous migration (for example Oracle/PostgreSQL to Azure), and modernisation tooling. Understanding of enterprise-grade non-functional requirements — high availability, disaster recovery, performance tuning, security, resilience, and cost optimisation — across both mission-critical databases and analytics platforms (including Fabric capacity sizing and workload/cost management).

Familiarity with AI grounding on databases, including vector stores, RAG, and connecting operational data to AI and Copilot experiences. Understanding of data engineering, data warehousing, analytics, governance, and modern data architectures across cloud-native and hybrid environments. Ability to communicate complex technical concepts clearly to both technical and non-technical audiences.