Key Responsibilities:

1. Technical Solutioning & Architecture:

- Lead technical discovery sessions, customer workshops, and requirement-gathering discussions.

- Design end-to-end solutions across Databricks, Snowflake, AWS, Azure, data engineering, and enterprise AI workloads.

- Develop scalable architectures for data ingestion, transformation, integration, analytics, and cloud modernization.

- Support AI, Generative AI (GenAI), Agentic AI, and ontology/context-driven use cases in collaboration with the CTO.

- Understand enterprise data landscapes, including SAP systems, SAP data mapping, and integration with modern data platforms.

- Create architecture diagrams, technical designs, implementation roadmaps, assumptions, dependencies, and risk assessments.

- Build reusable reference architectures, frameworks, accelerators, and solution components.

2. Presales & Customer Engagement:

- Lead technical presales activities and solution discovery for prospective and existing customers.

- Collaborate with the VP Growth & Revenue and sales teams to qualify opportunities and develop solution approaches.

- Translate business requirements into technical architectures, proposals, and implementation plans.

- Support RFP/RFI responses, technical presentations, solution walkthroughs, and customer discussions.

- Prepare and validate technical inputs for Statements of Work (SOWs), including scope, deliverables, assumptions, dependencies, and timelines.

- Build demos, prototypes, proof of concepts, and focused pilots to demonstrate solution feasibility and business value.

3. Estimation, Budgeting & Commercial Awareness:

- Prepare project effort estimates, resource plans, timelines, and implementation cost assessments.

- Develop realistic estimates based on technical scope, complexity, team composition, infrastructure requirements, and delivery approach.

- Identify risks, dependencies, assumptions, and potential scope changes that may affect delivery costs.

- Partner with business and delivery stakeholders to ensure proposals are commercially viable.

- Support pricing discussions, margin protection, and cost optimization through sound technical decisions.

4. Hands-on Delivery & Billability:

- Maintain meaningful billability by contributing directly to customer engagements and technical delivery.

- Balance billable project work with priority presales, discovery, and solution development activities.

- Build and validate prototypes, technical components, data pipelines, and focused implementation pilots where required.

- Support delivery teams in resolving architectural challenges and validating technical decisions.

- Ensure a smooth handoff from presales to delivery, with clear documentation of scope, architecture, estimates, risks, and customer expectations.

5. Collaboration & Technical Leadership:

- Work closely with the CTO on architecture decisions, emerging AI use cases, and technical solution validation.

- Collaborate with the VP Growth & Revenue and Directors on priority opportunities and customer engagements.

- Bridge the gap between business objectives, technical feasibility, and commercial outcomes.

Required Skills & Qualifications:

- 8 - 12+ years of experience in data engineering, cloud architecture, solution architecture, or enterprise technology consulting.

- Strong expertise in Databricks and/or Snowflake, with a solid understanding of modern data platforms.

- Strong understanding of data engineering, data integration, ETL/ELT, data warehousing, and cloud-based architectures.

- Experience with Azure and/or AWS; GCP exposure is an advantage.

- Mandatory prior experience in technical presales, solution consulting, or customer-facing solution architecture.

- Mandatory experience in effort estimation, project budgeting, resource planning, and costing.

- Understanding of SAP systems, enterprise data mapping, and integration with modern data platforms.

- Exposure to AI, GenAI, Agentic AI, RAG, or ontology/context-driven enterprise use cases.

- Strong customer communication, presentation, stakeholder management, and commercial awareness.

Preferred Qualifications:

- Experience with SAP S/4HANA, SAP BW, or other SAP data ecosystems.

- Experience integrating SAP data with Databricks, Snowflake, or cloud data platforms.

- Exposure to enterprise AI architecture, knowledge graphs, semantic layers, ontologies, and contextual data systems.

- Relevant Databricks, Snowflake, AWS, or Azure certifications.

- Experience developing reusable solution accelerators and leading enterprise pilot engagements.