Pre-sales Solutions Architect (Databricks)

This newly created role will see you become a key player in our Presales and solutioning team, owning the technical narrative for Databricks-centric opportunities across our pipeline. You will partner with Sales, Practice Leads, and Delivery to win Lakehouse, data engineering, ML, and GenAI deals on Databricks — from first technical conversation through to signed SOW. You will work closely with prospective clients to understand their data estate, workloads, and business goals, then design tailored Databricks-based architectures and drive the technical sales motion: discovery, demos, POCs, RFP responses, and commercial shaping.

The role demands strong Databricks platform fluency, broad cloud (AWS / Azure / GCP) literacy, commercial awareness, and the ability to make complex architectures land with both engineers and the C-suite. Databricks solution advocacy: articulate the value of the Databricks Data Intelligence Platform — Lakehouse architecture, Delta Lake, Unity Catalog, Photon, Databricks SQL, Workflows, MLflow, Mosaic AI, and Databricks Apps — tailoring presentations and demos to each prospect's business and technical context. Technical consultation: act as the trusted Databricks expert during sales engagements, handling deep technical questions, addressing objections, and positioning Databricks against alternatives (Snowflake, Microsoft Fabric, native cloud data warehouses, open-source Spark / Iceberg stacks).

Requirements

gathering: lead structured discovery with data, analytics, and platform teams to capture current-state architecture, workloads, governance constraints, and target outcomes; translate findings into prioritised solution options. High Level Solution design: design Databricks-based reference architectures across AWS, Azure, and GCP, covering ingestion, Lakehouse modelling (Bronze / Silver / Gold), Unity Catalog governance, MLOps with MLflow, GenAI patterns (RAG, fine-tuning, agentic), and cost / performance optimisation. Demos and POCs: build and run compelling demos and time-boxed POCs on Databricks that prove value against agreed success criteria.

RFPs and proposals: own the technical content of RFP / RFI responses, technical proposals, architecture diagrams, and Statements of Work in collaboration with the Commercial and Delivery teams; ensure SOWs are accurate, deliverable, and commercially sound. Cross-functional collaboration: act as the connective tissue between Sales, Practice Leads (Data Engineering, ML, GenAI, Platform), and Delivery, ensuring proposed solutions are deliverable, commercially sensible, and aligned to capacity and timelines. Partner alignment: work hand-in-glove with Databricks field teams — account executives, specialist SAs, partner managers — on joint pursuits, co-selling motions, and partner programmes.

Delivery embedment: Periodically, you will be embedded with the delivery team in a billable role on client engagements — typically as a Databricks Solutions Architect during design or mobilisation phases — keeping you close to the platform and ensuring pre-sales commitments land cleanly in delivery. Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, or related field. Proven experience in technical pre-sales, solutions architecture, or hands-on consulting on the Databricks platform (or a comparable Lakehouse / big data stack).

Strong working knowledge of the Databricks Data Intelligence Platform: Lakehouse architecture, Delta Lake, Apache Spark, Unity Catalog, Databricks SQL, Workflows, MLflow, Mosaic AI / model serving, and at least one cloud deployment (AWS, Azure, or GCP). Confident designing and presenting Lakehouse and data platform architectures, including ingestion patterns, medallion modelling, governance, MLOps, and GenAI workloads (RAG, fine-tuning, agentic patterns).