Key Responsibilities:

Client engagement and discovery:

- Lead technical and business discovery conversations with CXOs, data leaders and architects to understand goals, constraints and the current data landscape.

- Drive credible, consultative technical discussions on data platforms, analytics and AI use cases, and position our point of view against client priorities.

Solution architecture:

- Design end-to-end solutions and reusable frameworks across data ingestion, lakehouse, data modelling, governance, BI and AI/ML on Azure + Databricks or AWS + Databricks.

- Define target-state architectures, migration and modernisation roadmaps, and effort and delivery approaches that delivery teams can execute.

- Run solution reviews, proof-of-concepts and demos with technical and business stakeholders.

Deal ownership, discovery to closure:

- Own the pursuit end to end: discovery, solution design, estimation, proposal or RFP response, pricing inputs, negotiation support and closure.

- Work with Sales, Delivery, Practice and Finance leaders to build winning, deliverable and profitable proposals.

- Maintain pipeline hygiene, deal qualification and win/loss insights.

Practice and delivery enablement:

- Build solution accelerators, reference architectures, case studies and pitch material for the practice.

- Hand over won deals smoothly to delivery, and stay engaged as a trusted advisor to protect scope, quality and client relationships.

- Track hyperscaler and Databricks roadmap changes, and work with partner teams on joint pursuits.

Required Qualifications and Skills:

- 5 - 8 years of experience, with a strong IT Services or consulting background preferred.

- Proven experience in pre-sales, solutioning or solution management for Data Engineering, Data Analytics and AI engagements.

- Strong understanding of modern data architecture: lakehouse, ELT/ETL, streaming, data modelling, data governance and BI.

- Working experience with Azure + Databricks or AWS + Databricks (for example Delta Lake, Unity Catalog, Spark, workflows and MLflow).

- Demonstrated ability to architect solutions and frameworks, not just describe them.

- Track record of owning deals end to end: discovery, solution, proposal, pricing and closure.

- Excellent communication and presentation skills, with the confidence to lead technical conversations with client architects and senior business stakeholders.

- Strong written skills for proposals, RFP responses and solution documents.

- Ability to work across Sales, Delivery and Practice teams and manage multiple pursuits at once.

Good to Have:

- Hands-on technical experience: PySpark, SQL, Python, or building PoCs and demos on Databricks.

- Exposure to Generative AI, ML and MLOps use cases on Databricks, Azure or AWS.

- Databricks, Azure or AWS certifications (for example Databricks Data Engineer / Architect, Azure Data Engineer, AWS Data Analytics).

- Experience with tools such as Azure Data Factory, Synapse or Fabric, AWS Glue or Redshift, dbt, Power BI or Tableau.

- Experience with cloud migration and data modernisation programmes, such as moving from legacy warehouses to a lakehouse.

- Exposure to Databricks or hyperscaler partner ecosystems and co-sell motions.