Solution Architect, Data Platform & AI ArchitectureRemote Permanent PositionSalary: 200K +Keys to the position: 10+ years of software engineering experience, including 5+ years designing distributed systems and solution architectures
Experience designing cloud-native, multi-tenant SaaS platforms and building or evolving enterprise Data Platforms
Experience with Kafka, modern data platforms such as Snowflake, Databricks, Amazon Redshift, Apache Iceberg, Delta Lake, modern data engineering and observability technologies such as dbt, Airflow, OpenSearch, DataHub, OpenMetadata, OpenLineage, or OpenTelemetry.
Experience with REST APIs, GraphQL, API design, and integration architecture.
As a Solution Architect, Data Platform & AI within Platform Engineering, you will help define and evolve the architecture behind the enterprise Data Platform, AI capabilities, and reusable platform services.
Reporting to the Senior Director, Engineering for Platform, you will partner across Product Management, Engineering, Data & Analytics, Security, and Architecture to turn complex business and technical challenges into scalable, secure, observable, and reusable platform capabilities.
Your influence will extend well beyond individual solutions. You will help establish durable business models, reference architectures, engineering standards, and reusable patterns that enable teams across Frontline to move faster while strengthening the foundation for data-driven products, analytics, and AI-enabled experiences.
Success in this role is measured not only by the quality of the architecture you design, but also by whether teams can adopt it successfully and whether the resulting capabilities improve reliability, engineering productivity, customer outcomes, and long-term platform value.
How You'll Drive SuccessShape the Data Platform architecture- Define and evolve the architecture for the enterprise Data Platform, supporting analytics, integrations, operational workloads, data products, and AI-enabled experiences.
- Establish reusable patterns for data ingestion, transformation, storage, publication, event streaming, and data exchange across products.
- Define canonical business models, data contracts, versioning approaches, domain boundaries, and shared business concepts that improve consistency across the portfolio.
- Establish approaches for metadata, lineage, cataloging, discoverability, governance, data quality, observability, and secure data access.
- Enable reusable data products that can be consumed through APIs, events, analytical interfaces, and AI services.
Enable responsible, scalable AI - Architect reusable capabilities that allow product and engineering teams to adopt AI safely, responsibly, and efficiently.
- Shape architecture for AI-ready data, retrieval and semantic search, agentic workflows, model and tool integrations, context and knowledge systems, AI evaluation, observability, governance, security, and human oversight.
- Establish reusable integration and governance patterns that reduce isolated implementations and make trusted AI capabilities easier for teams to adopt.
- Evaluate emerging AI technologies thoughtfully, balancing innovation with security, explainability, maintainability, operational maturity, and customer trust.
- Use AI-assisted tools to accelerate architectural analysis, documentation, design exploration, research, and solution evaluation while applying strong judgment to validate outputs and protect sensitive information.
Build the platform as a product - Partner with Platform Product Management during discovery and strategic planning to identify reusable capabilities that solve meaningful problems across engineering teams.
- Help translate business and customer needs into scalable technical approaches and long-term platform investments.
- Inform platform roadmaps, architectural priorities, and build-versus-buy decisions using engineering impact, adoption, usability, cost, risk, and business value.
- Design platform capabilities that are intuitive, discoverable, well documented, and easy for engineering teams to adopt.
- Advance self-service approaches for data publishing, events, APIs, platform capabilities, and developer tooling.
- Use adoption, usability, engineering productivity, reliability, and operational outcomes to evaluate whether platform investments are creating value.
Lead architecture through influence - Create and maintain reference architectures, Architecture Decision Records, Design Sketches, technical standards, engineering guidelines, reference implementations, and reusable architectural patterns.
- Participate in Architecture Review Board activities and help teams make thoughtful architectural decisions without creating unnecessary gates to delivery.
- Provide architectural guidance and mentorship to Technical Leads, engineers, and cross-functional partners.
- Communicate complex technical concepts and tradeoffs clearly to technical and non-technical audiences.
- Bring teams together around shared architectural direction, inviting different perspectives and creating clarity when problems cross organizational boundaries.
- Take ownership of architectural risks, gaps, dependencies, and opportunities, and drive them toward resolution.
Partner from discovery through operations - Work closely with Product Management, UX, Engineering, QA, Security, DevOps, Data & Analytics, and Architecture throughout discovery, design, refinement, planning, delivery, and production readiness.
- Help teams make pragmatic tradeoffs between immediate business priorities and long-term platform sustainability.
- Design for reliability, scalability, security, performance, cost optimization, disaster recovery, multi-tenant SaaS operations, observability, telemetry, data freshness, data quality, and Service Level Objectives.
- Champion operational excellence as part of architecture from the beginning, not as an afterthought.
- Learn what matters most to the teams and customers affected by platform decisions, and solve for the outcomes they need rather than simply implementing individual requests.
Job Requirements
What You Bring to Help Us Grow- Bachelor's degree in Computer Science, Engineering, or a related discipline, or equivalent practical experience.
- 10+ years of software engineering experience, including 5+ years designing distributed systems and solution architectures.
- Experience designing cloud-native, multi-tenant SaaS platforms and building or evolving enterprise Data Platforms.
- Strong understanding of distributed systems, event-driven architectures, streaming, asynchronous workflows, APIs, integrations, and data contracts.
- Experience defining data models, canonical business concepts, governance approaches, metadata, lineage, observability, and secure data access patterns.
- Strong experience with AWS cloud services and Infrastructure as Code.
- Experience with Kafka or equivalent streaming and messaging technologies.
- Experience with modern data platforms such as Snowflake, Databricks, Amazon Redshift, Apache Iceberg, Delta Lake, or comparable technologies.
- Experience with modern data engineering and observability technologies such as dbt, Airflow, OpenSearch, DataHub, OpenMetadata, OpenLineage, or OpenTelemetry.
- Experience with REST APIs, GraphQL, API design, and integration architecture.
- Experience collaborating with Product Management during discovery, solution design, and roadmap development.
- Strong understanding of software architecture principles, security architecture, engineering best practices, and operational excellence.
- Excellent written and verbal communication, facilitation, mentoring, and technical leadership skills.
- Experience influencing technical direction across teams and organizational boundaries without relying on direct authority.
E
xperience in one or more of the following areas will help you make an even greater impact:- Retrieval-Augmented Generation, vector search, semantic search, or knowledge retrieval.
- Agentic AI systems, agent orchestration, and tool integration.
- Model Context Protocol and emerging AI integration patterns.
- Knowledge graphs and context management systems.
- AI evaluation, observability, governance, and security.
- Domain-Driven Design, Event Sourcing, or CQRS.
- Enterprise architecture practices.
- Platform engineering organizations and developer enablement.
- Responsible use of AI-assisted or agentic development workflows in professional engineering environments.
What You'll Need to Thrive- You take ownership of outcomes, follow through on commitments, and proactively address architectural risks and opportunities.
- You think in systems, connecting data, platforms, products, architecture, engineering teams, and customer experiences rather than optimizing one component in isolation.
- You balance technical excellence and long-term sustainability with pragmatic delivery.
- You build trust through collaboration, invite different perspectives, share what you know, and help teams arrive at stronger solutions together.
- You seek to understand what customers and adopting teams are ultimately trying to accomplish and use that understanding to guide technical decisions.
- You are comfortable challenging assumptions, learning from evidence, and improving an approach when a better path becomes clear.
- You communicate complex ideas with clarity and adapt your approach for technical, product, business, and executive audiences.
- You are curious about emerging technologies and actively experiment with new tools and approaches while applying thoughtful judgment.
- You use AI as a practical partner for analysis, exploration, documentation, and problem-solving, while validating outputs and maintaining appropriate security, privacy, and governance.
- You are comfortable navigating ambiguity and helping others create clarity.
- You enjoy mentoring engineers, strengthening technical practices, and helping organizations scale.
- You measure architecture by what it enables: stronger teams, more reliable platforms, faster delivery, trusted customer experiences, and better outcomes.
