Senior Solution Architect - Data Engineering
First seen by Alion on Oct 27, 2026.
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 :
1. 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 problem-solving skills.
- Experience owning solution delivery from requirements gathering through production adoption.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
B. 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.
5. 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.
6. 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.
7. Expected Deliverables :
- Customer discovery outcomes, documented requirements, and solution architecture designs.
- 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.
8. 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 requirements.
Ideal Candidate Profile :
The ideal candidate is a hands-on architect who can confidently engage with customers, understand business problems, design scalable data and AI solutions, and personally contribute to implementation. The candidate should combine strong Snowflake and SQL expertise with modern data transformation practices, semantic modeling, AI-assisted development, and production delivery experience.
This is not a purely conceptual architecture role. It requires an individual who can bridge business and technology, demonstrate solutions through working prototypes, guide engineering teams, and take accountability for successful customer outcomes.
Primary Skills :
- Snowflake
- SQL
- dbt
- SQLMesh
- Data Architecture
- Semantic Modeling
- AI-Assisted Development
Secondary Skills : Salesforce Integration, Python, APIs, LLMs, AI Agents, RAG, CI/CD
Skills
Solution Architect, Solution Design, Data Engineering, Snowflake DB, Data Integration, Semantic Modeling, AI Integration, Python, SQL, Data Build Tool