Senior Solution Architect

Job Summary

Job Description: Snowflake Solution Architect – Data & AI
Location: Noida / Chennai / Bangalore | Experience: 10+ years

Position Overview:

We are looking for a highly skilled and customer-focused Snowflake & Databricks Solution Architect to support Data & AI solutioning initiatives for enterprise customers. The role will be responsible for creating differentiated, scalable, secure, and business-aligned solution responses for RFPs, RFIs, RFQs, proactive proposals, client workshops, Snowflake modernization programs, Databricks lakehouse transformation engagements, cloud data platform modernization, AI/ML enablement programs, and strategic deal pursuits.
The ideal candidate should have strong experience in Snowflake architecture, Databricks architecture, cloud data platforms, data warehouse modernization, data lake and lakehouse modernization, ETL/ELT modernization, data engineering, data governance, Data Ops, MLOps, , AI-ready data platforms, Snowpark, Snowflake Cortex AI, Databricks Unity Catalog, Delta Lake, and managed platform operations.
This is an individual contributor role requiring strong solutioning, consulting, technical architecture, and stakeholder management capabilities across modern Data & AI ecosystems on Snowflake and Databricks deployed across AWS, Microsoft Azure, and Google Cloud Platform..

Role Purpose:

The Snowflake & Databricks Solution Architect will work closely with sales, presales, delivery, consulting, Snowflake partner teams, Databricks partner teams, hyperscaler teams, and client stakeholders to define end-to-end cloud data platform, lakehouse, data engineering, analytics, and AI-ready data solutions.
The role will support business growth by shaping high-quality proposal responses, solution blueprints, modernization strategies, migration approaches, lakehouse and cloud data platform architecture diagrams, delivery models, reusable solution assets, managed operations models, and transformation roadmaps aligned to client business outcomes.

Key Responsibilities

• Key Responsibilities
• Lead and contribute to end-to-end presales solutioning activities for Snowflake and Databricks modernization, migration, implementation, optimization, AI-ready data platform, and managed services opportunities.
• Analyze client RFPs, RFIs, RFQs, business requirements, current-state data landscape, legacy platform constraints, pain points, workload characteristics, compliance needs, data governance requirements, and evaluation criteria to design fit-for-purpose Snowflake and Databricks solutions.
• Create solution responses covering technical approach, target architecture, Snowflake platform design, Databricks lakehouse architecture, migration approach, data engineering patterns, AI/ML enablement, integration strategy, governance approach, implementation roadmap, assumptions, dependencies, risks, and differentiators.
• Architect enterprise Databricks solutions covering data ingestion, data lake modernization, lakehouse architecture, Delta Lake, medallion architecture, ETL/ELT transformation, batch and streaming pipelines, analytics consumption, machine learning, GenAI enablement, feature engineering, and governed data sharing.
• Develop proposal-ready architecture narratives, logical architecture diagrams, data flow diagrams, migration architecture views, deployment views, Snowflake workload patterns, Databricks lakehouse workload patterns, AI-ready data platform blueprints, and technology stack recommendations.
• Support solution estimation by defining work breakdown structures, delivery phases, migration factory approach, team composition, assumptions, dependencies, accelerators, and high-level effort inputs.
• Collaborate with delivery teams to ensure proposed Snowflake and Databricks solutions are practical, scalable, secure, governed, cost-optimized, implementable, and aligned with HCLTech delivery capabilities.
• Participate in client workshops, discovery sessions, Snowflake readiness assessments, Databricks readiness assessments, lakehouse modernization workshops, migration planning workshops, solution walkthroughs, technical presentations, and proposal defense discussions.
• Contribute to reusable assets, solution playbooks, reference architectures, estimation models, proposal templates, migration factory patterns, modernization assessment templates, Snowflake best practices, Databricks best practices, and industry-specific solution accelerators.
• Stay updated on emerging trends in Snowflake, Databricks, Lakehouse architecture, Snowpark, Snowflake Cortex AI, Snowflake Intelligence, Unity Catalog, Delta Lake, Delta Sharing, Delta Live Tables, AI-enabled data engineering etc.
• Collaborate with marketing and presales teams in developing collateral, case studies, reusable content, and thought leadership materials..
• Principal KPIs for the Role
• Contribution to strategic deal wins
• Quality and timeliness of RFP/RFI responses
• Customer solution acceptance and feedback
• Innovation and value-added solution recommendations
• Alignment of Snowflake and Databricks solutions to customer business outcomes, AI-readiness, governance, modernization, cost optimization, and enterprise data strategy.
• Effectiveness of proposed Snowflake and Databricks migration, lakehouse modernization, optimization, and managed services approaches.

Skill Requirements

• Core Competencies & Technical Expertise
• Hands-on experience in preparing solution responses, architecture documents, presentations, technical proposals, migration approaches, operating models, and implementation roadmaps.
• Experience responding to RFPs/RFIs/RFQs and creating solution narratives, architecture diagrams, delivery approaches, modernization strategies, migration estimates, governance models, and managed services constructs.
• Experience with Snowflake and Databricks deployment and integration across cloud platforms such as Microsoft Azure, AWS, and Google Cloud Platform.
• Strong understanding of migration patterns from legacy data platforms to Snowflake and Databricks, including workload assessment, data lake modernization, schema conversion, ETL/ELT rationalization, data pipeline migration, data reconciliation, testing automation, parallel run, cutover strategy, performance tuning, and post-migration optimization.
• Knowledge of BI and analytics tools such as Power BI, Tableau, Qlik, Looker, Thought Spot, or Sigma from a Snowflake and Databricks consumption and semantic layer perspective.
• Strong communication and presentation skills with experience interacting with customer stakeholders, CDO organizations, CIO teams, enterprise architects, data platform teams, analytics leaders, security teams, AI/ML teams, and business stakeholders.
• Ability to work independently across multiple concurrent opportunities in a fast-paced presales environment.
• Required Qualifications
• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.
• Preferred Certifications: Snow Pro Advanced: Architect or Snow Pro Advanced: Data Engineer certification will be highly preferred.
• Databricks Certified Data Engineer Professional, Databricks Certified Data Architect or Professional certification will be strongly preferred.
• Equivalent certifications across AWS, Azure, GCP platforms will also be beneficial.
• Experience in presales solutioning, proposal development, consulting, or customer-facing architecture roles is strongly preferred.
• Minimum 10 years of experience in Data & AI technologies, with relevant experience in Snowflake architecture, Databricks architecture, cloud data platform modernization, data engineering, solution architecture, AI/ML enablement, and presales solutioning.

Other Requirements

• Principal KPIs for the Role
• Contribution to strategic deal wins
• Quality and timeliness of RFP/RFI responses
• Customer solution acceptance and feedback
• Innovation and value-added solution recommendations
• Alignment of Snowflake and Databricks solutions to customer business outcomes, AI-readiness, governance, modernization, cost optimization, and enterprise data strategy.
• Effectiveness of proposed Snowflake and Databricks migration, lakehouse modernization, optimization, and managed services approaches.