Senior Solution Architect

If interested please share your Resume @

[email removed]

Role: Senior Solution Architect

Experience : 12+ years

Location: Hyderabad

Mode of Work: Hybrid (3 days WFO)

Shift Timings: 3:00 PM 12:00 AM (IST)

Position Overview

Position: Senior Solutions Architect – Data

Role Type: Customer-Facing Architecture and Hands-on Technical Delivery.

About the Role

We are seeking a Senior Solutions Architect – Data to lead customer engagements and deliver end-to end data and AI solutions, from discovery and solution design through implementation, production deployment, and adoption.

The ideal candidate combines strong customer-facing solution architecture skills with hands-on expertise in data engineering, Snowflake, modern data transformation frameworks, semantic modeling, and AI-enabled analytics. The role requires translating complex business requirements into scalable, secure, and cost-effective technical solutions while accelerating development through AI-assisted coding tools.

The candidate will work closely with business stakeholders, executives, data teams, and technology leaders to build trusted data foundations, enable self-service analytics, and deliver AI-driven business outcomes across Go-to-Market (GTM), revenue operations, and enterprise functions.

Key Responsibilities

Customer Engagement and Solution Architecture

• Lead customer discovery sessions, technical workshops, and stakeholder interviews to understand business processes, data challenges, and desired outcomes.
• Translate business requirements into solution architectures, implementation roadmaps, technical specifications, and measurable success criteria.
• Present architectural decisions, technical trade-offs, and recommendations to business users, technology teams, and executive stakeholders.
• Own the technical solution lifecycle from proof of concept (PoC) and prototyping through production deployment and customer adoption.
• Identify opportunities to improve business processes through data modernization, analytics, and AI-enabled workflows.

Data Engineering and Snowflake Architecture

• Design and implement end-to-end data solutions covering ingestion, transformation, data modeling, semantic layers, analytics, and AI applications.
• Develop and optimize advanced SQL queries, Snowflake data models, transformation pipelines, and incremental processing workflows.
• Implement scalable data architectures with appropriate performance optimization, access controls, data quality checks, and cost management.
• Design reliable pipelines with testing, documentation, lineage, observability, and deployment controls.
• Establish engineering best practices for data reliability, maintainability, security, and production operations.

Data Transformation and Integration

• Develop and maintain data transformation workflows using dbt and SQLMesh, including models, dependencies, tests, environments, and deployment strategies.
• Integrate Salesforce and other enterprise applications to establish trusted data models for accounts, opportunities, contracts, subscriptions, and customer activities.
• Build data ingestion and integration workflows using Python, REST APIs, and tools such as Fivetran or Airbyte.
• Implement appropriate reconciliation, data validation, error handling, and monitoring mechanisms.
• Collaborate with source-system owners to resolve data quality issues and ensure consistency across integrated platforms.

Semantic Modeling and AI-Powered Analytics

• Design governed semantic layers that define business metrics, entities, relationships, terminology, and approved calculation logic.
• Enable natural-language analytics and conversational data exploration using Snowflake Cortex Analyst and related AI capabilities.
• Ensure business metrics are consistent, traceable, and validated across reporting, analytics, and AI applications.
• Validate AI-generated SQL for correctness, business alignment, security, and performance.
• Implement appropriate grounding, evaluation, access controls, and guardrails to improve the reliability of AI-generated insights.

AI-Assisted Development and Engineering Productivity

• Use AI-assisted development tools such as Cursor, Claude Code, OpenAI Codex, or equivalent tools to accelerate prototyping, coding, testing, debugging, and documentation.
• Apply AI-assisted engineering practices while maintaining code quality, security, maintainability, and compliance with customer requirements.
• Develop and integrate AI-enabled workflows using LLMs, AI agents, retrieval-augmented generation (RAG), and enterprise data sources.
• Establish disciplined practices for code review, automated testing, version control, and protection of sensitive customer information.
• Evaluate emerging AI development capabilities and identify opportunities to improve engineering productivity and solution delivery.

Solution Demonstration and Production Delivery

• Build functional prototypes and business-focused demonstrations to validate solution feasibility and communicate business value.
• Incorporate customer feedback and iteratively refine solutions before production implementation.
• Own technical troubleshooting, user acceptance testing (UAT), release planning, go-live readiness, and production stabilization.
• Implement Git-based development workflows, CI/CD pipelines, environment management, and controlled releases.
• Communicate technical progress, dependencies, risks, issues, and mitigation plans to customer and internal stakeholders.

Customer Adoption and Business Value

• Lead customer onboarding, technical enablement, training, and knowledge transfer.
• Drive adoption of delivered data products, analytics solutions, and AI-enabled workflows.
• Define and track adoption metrics, usage patterns, operational improvements, and business outcomes.
• Identify opportunities for continuous improvement, solution expansion, and additional data and AI use cases.

Required Skills and Qualifications

Primary Technical Skills

Solution Architecture and Customer Engagement

• Strong experience in customer-facing solution architecture, technical consulting, or solution engineering.
• Proven ability to lead discovery workshops and translate ambiguous business requirements into practical technical solutions.
• Excellent communication, stakeholder management, presentation, and technical problemsolving skills.
• Experience owning solution delivery from requirements gathering through production adoption.

Snowflake and Advanced SQL

• Deep hands-on experience with Snowflake architecture, data engineering, and analytics solutions.
• Strong command of advanced SQL, including complex joins, CTEs, window functions, query optimization, and performance tuning.
• Experience with dimensional modeling, incremental processing, data transformations, and large-scale analytical workloads.
• Knowledge of Snowflake security, role-based access control, workload optimization, and cost management.
• Understanding of Snowflake Cortex capabilities and their application to enterprise analytics and AI use cases.

dbt and SQLMesh

• Strong hands-on experience with dbt for modular data transformations, reusable models, testing, documentation, and dependency management.
• Working knowledge of SQLMesh, including model definitions, incremental processing, environments, testing, and deployment.
• Understanding of version-controlled data development, CI/CD, data quality validation, and production release management.

Data Integration and Engineering

• Experience integrating Salesforce and other enterprise systems with analytical data platforms.
• Knowledge of Salesforce objects and business entities, including accounts, contacts, leads, opportunities, contracts, and subscriptions.
• Proficiency in Python, REST APIs, and data integration patterns.
• Experience with ingestion and ELT platforms such as Fivetran, Airbyte, or equivalent tools.
• Ability to design reliable, scalable, and maintainable data pipelines.

Semantic Modeling and Business Analytics

• Practical experience defining business metrics, dimensions, entities, relationships, and reusable semantic models.
• Understanding of governed metrics and semantic layers for BI, self-service analytics, and natural-language querying.
• Ability to validate generated SQL and ensure analytical outputs align with approved business definitions.
• Experience translating business terminology into technical data models and analytical logic.

AI, LLMs, and AI-Assisted Development

• Working knowledge of LLMs, prompt engineering, AI agents, RAG, and natural-language analytics.
• Understanding of grounding, evaluation, hallucination mitigation, data privacy, and AI guardrails.
• Hands-on experience with AI coding assistants such as Cursor, Claude Code, Codex, or equivalent tools.
• Ability to use AI-assisted development responsibly with code reviews, automated tests, and secure engineering practices.

1. Software Engineering and Production Delivery

• Strong working knowledge of Git, branching strategies, pull requests, CI/CD, and automated testing.
• Experience managing development, testing, staging, and production environments.
• Understanding of application security, observability, troubleshooting, release management, and production support.

Business and Domain Knowledge

• Understanding of Go-to-Market (GTM), sales operations, revenue operations, and enterprise business processes.
• Ability to collaborate with business teams to define KPIs, reporting requirements, and operational metrics.
• Experience connecting business objectives with data architecture and AI implementation strategies.

Preferred Domain Knowledge

Experience in one or more of the following functional areas is highly desirable:

Sales and Revenue Operations

• Sales pipeline management, forecasting, conversion rates, bookings, opportunity tracking, and renewals. Finance
• Annual recurring revenue (ARR), monthly recurring revenue (MRR), revenue movements, financial reconciliation, and reporting.

Marketing

• Campaign performance, marketing attribution, funnel conversion, customer acquisition costs, and campaign ROI. Customer Success
• Customer health scoring, churn prediction, retention, renewal forecasting, and account expansion.

Professional Services

• Resource utilization, project delivery, services revenue, billing, delivery efficiency, and project profitability.

Preferred Additional Qualifications

• Experience delivering enterprise data platforms and AI-enabled analytics solutions for external customers.
• Exposure to Snowflake Cortex Analyst, Cortex AI, or comparable natural-language analytics capabilities.
• Familiarity with modern BI and analytics platforms and governed metric definitions.
• Experience with data observability, lineage, governance, and enterprise data quality frameworks.
• Experience delivering proofs of concept and converting them into production-grade enterprise solutions.
• Relevant cloud data platform or architecture certifications are an advantage.
• Customer discovery outcomes, documented requirements, and solution architecture designs.

Expected Deliverables

• Production-ready Snowflake data models, transformation pipelines, and integration workflows.
• Governed semantic models and validated business metrics for analytics and AI use cases.
• Functional prototypes, technical demonstrations, and production deployments.
• Automated testing, documentation, CI/CD workflows, and operational support procedures.
• Customer onboarding materials, training, and adoption measurement.
• Demonstrable improvements in data reliability, analytics accessibility, engineering productivity, and business outcomes.

Success Measures

• Successful conversion of business requirements into production-ready solutions.
• Data quality, reliability, performance, security, and cost efficiency of delivered solutions.
• Accuracy and consistency of semantic models, business metrics, and AI-generated analytical outputs.
• Reduction in prototype-to-production delivery time through effective engineering practices.
• Customer satisfaction, solution adoption, and measurable business value.

Compliance with engineering standards, delivery timelines, and production readiness