Senior Forward Deployed Engineer

Spaulding Ridge is an advisory and IT implementation firm. We help global organizations get financial clarity into the complex, daily sales, and operational decisions that impact profitable revenue generations, efficient operational performance, and reliable financial management. At Spaulding Ridge, we believe all business is personal. Core to our values is our relationships with our clients, our business partners, our team, and the global community. Our employees dedicate their time to helping our clients transform their business, from strategy through implementation and business transformation. What You Will Do and Learn We’re looking for a hands-on Senior Forward Deployed Engineer to join Spaulding Ridge’s Data Solutions practice. You will work directly alongside client business users and technical teams to understand their workflows, uncover high-value problems and build solutions they use in day-to-day operations. Working across Snowflake, data engineering, APIs, analytics, applications and AI-enabled workflows, you will take solutions from discovery and rapid prototyping through production deployment and adoption. You will test assumptions with users, make pragmatic engineering decisions and measure results against agreed business outcomes. This includes identifying where conventional automation, AI assistance or agent-based approaches best fit the problem. This is a manager-grade technical leadership role with sustained responsibility for architecture, coding and troubleshooting. You will guide engineers and own technical delivery, partnering with engagement leadership on overall scope, staffing, project budgets and client commitments. Client work may include Office of the CFO workflows such as reconciliation, financial reporting, planning and variance analysis, alongside other enterprise use cases. • Work closely with end users to observe workflows, investigate pain points and agree priorities, success measures and a practical first release, including when requirements are unclear. • Build working prototypes quickly, demonstrate them to users and iterate on feedback. Decide which ideas to develop, change or stop before committing to production delivery. • Design and implement solutions across source systems, ingestion, Snowflake, transformation, semantic layers, APIs and user-facing applications. Personally code and debug critical components. • Own Snowflake architecture decisions covering data models, warehouse configuration, ingestion, role-based access, data sharing and environments. Optimize performance, reliability and running costs. • Build reliable pipelines and transformation models, with data quality checks, reconciliation and governed metrics. Deliver reporting and analytics where they support the client’s workflow. • Develop applications and APIs with authentication, secure data access, logging and error handling. Build workflows that help users investigate issues, make decisions and take action. • Build and integrate LLM-enabled applications and tool-using agents where they improve client outcomes. Apply retrieval-augmented generation (RAG), enterprise data access and workflow orchestration where appropriate, choosing the simplest effective solution. • Establish appropriate controls for AI workflows, including context management, tool permissions, human approvals, evaluation, tracing and failure recovery. Test accuracy and task completion against representative examples, and monitor cost and response time. • Use AI coding assistants and agents, such as Claude Code, OpenAI Codex, Snowflake CoCo or equivalents, for coding, testing, debugging and documentation. Review generated output, protect client information and remain accountable for engineering quality. • Take solutions into production through testing, CI/CD, monitoring and operational documentation. Resolve rollout issues and establish a clear support and ownership handover. • Support user adoption and measure results with client teams against agreed baselines, such as manual effort, processing time, reliability and cost. Use findings to improve the solution. • Lead technical priorities, estimates, dependencies and risks; communicate trade-offs to stakeholders and raise scope or delivery issues early. Coach engineers through design and code reviews. • Contribute technical discovery, scoping and demonstrations to proposals. Turn repeatable solutions into tested accelerators, application components, reusable agent tools and reference architectures for the practice. Essential qualifications We are looking for depth in Snowflake and data engineering, demonstrated application-building ability, practical AI engineering experience and sound technical delivery judgment. Candidates should be able to explain what they have built, how they tested it and how it performed in use. • 10+ years of relevant consulting or industry experience , including hands-on software or data engineering and delivery of production solutions for clients or business stakeholders. This refers to overall experience, not years working with AI or agent frameworks. • Strong hands-on Snowflake and SQL experience, including data modeling, ingestion, access controls, performance and cost optimization, and production implementation. • Experience building reliable data pipelines and transformation models using dbt or comparable tools, with appropriate orchestration, testing and reconciliation. • Strong programming skills in Python, JavaScript or TypeScript and a relevant back-end framework. Proven ability to deliver a usable application across the interface, API and data layers. Deep expertise in every front-end framework is not required. • Experience building APIs and integrations using REST, JSON and secure authentication such as OAuth, with appropriate authorization and error handling. • Demonstrated hands-on experience developing, integrating and testing an LLM-enabled application or tool-using agent workflow connected to busin