DE&A - Core - Cloud Data Engineering - AWS Data Engineering

Role Overview:

We are seeking an accomplished Pre-Sales & Technical Solution Head to lead Analytics and AI solutioning across hyperscaler ecosystems — AWS, Azure, and GCP.

This role will spearhead enterprise solution strategy, pre-sales engagement, and technical architecture for large-scale data and AI programs. The ideal candidate will blend deep cloud analytics expertise, customer advisory acumen, and leadership capability to drive growth, innovation, and market differentiation.

Key Responsibilities:

1. Solution Leadership & Pre-Sales Strategy

1. Own end-to-end pre-sales and solution architecture for Data, Analytics, and AI opportunities across hyperscalers.
2. Lead solution responses for RFPs/RFIs, executive presentations, and customer workshops.
3. Define reference architectures and accelerators for data modernization, MLOps, GenAI, and intelligent automation.
4. Collaborate with practice, delivery, and partner alliance teams to create winning, scalable solution propositions.
5. Drive go-to-market strategy and build solution playbooks tailored to verticals like High-Tech, BFSI, Healthcare, and Manufacturing.

2. Technical Expertise & Architecture

1. Architect cloud-native data platforms and AI ecosystems leveraging modern data stack technologies (Snowflake, Databricks, BigQuery, Synapse, Fabric).
2. Design end-to-end architectures covering data ingestion, transformation, governance, visualization, and AI/GenAI integration.
3. Evaluate and integrate GenAI frameworks (Azure OpenAI, Vertex AI, Amazon Bedrock, LangChain, LlamaIndex) into enterprise-grade solutions.
4. Ensure alignment with security, compliance, and cost optimization best practices.

3. Customer Engagement & Advisory

1. Act as a trusted advisor to client executives (CDO, CTO, CIO, Head of Data).
2. Conduct discovery and envisioning sessions to identify business challenges and translate them into actionable data & AI solutions.
3. Partner with sales and delivery leaders to define value propositions and ROI-driven solution outcomes.
4. Support executive-level presentations demonstrating innovation, scalability, and time-to-value benefits.

4. Partnership & Ecosystem Collaboration

1. Build deep alliances with hyperscaler partners (AWS, Microsoft, Google) and leverage their GTM programs and funding mechanisms.
2. Collaborate with partner engineering teams to co-create joint industry POVs and reference architectures.
3. Represent the organization in partner and industry forums showcasing innovation and thought leadership in Analytics & AI.

5. Leadership & Capability Building

1. Mentor and lead a global team of solution architects and pre-sales consultants.
2. Drive internal enablement on modern data and AI stacks across delivery and pre-sales functions.
3. Contribute to IP creation, solution frameworks, and reusable assets (e.g., GenAI accelerators, Data Buddy, ZensAI, etc.).

Required Skills & Experience:

1. 15+ years of experience in Data Engineering, Analytics, and AI, with 5+ years in pre-sales or solution leadership roles.
2. Deep understanding of hyperscaler ecosystems — AWS, Azure, and GCP — and their analytics & AI offerings.
3. Proven experience in solutioning large-scale transformation programs (>$10M+) for global enterprises.
4. Strong command over modern data architectures, data governance, AI/ML pipelines, and GenAI adoption frameworks.
5. Excellent executive communication, proposal writing, and storytelling skills.
6. Demonstrated success in partner-driven GTM and enterprise deal conversions.

Preferred Certifications:

1. AWS Certified Data Analytics / Machine Learning – Specialty
2. Microsoft Certified: Azure Data Engineer or AI Engineer Associate
3. Google Cloud Professional Data Engineer / ML Engineer
4. Databricks / Snowflake Solution Architect Certification
5. GenAI or LLM Application Development certification (OpenAI / Azure / GCP)

Key Success Metrics (KPIs):

1. Analytics & AI deal conversion rate and revenue influenced
2. Number of joint GTM wins with hyperscaler partners
3. Reusable solution assets and accelerators developed
4. Customer satisfaction and CXO feedback on solution workshops
5. Internal enablement score for analytics pre-sales and architecture teams