Senior Forward Deployed Engineer

Overview

Hands-on Senior Forward Deployed Engineer in Spaulding Ridge's Data Solutions practice, working directly with client business users and technical teams to build and deploy data, application and AI-enabled solutions, with manager-grade technical leadership.

What you'll do

• Work alongside client business users to understand workflows, uncover high-value problems and build solutions used in day-to-day operations.
• 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, iterate on feedback, and decide which ideas to develop, change or stop.
• 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.
• Develop applications and APIs with authentication, secure data access, logging and error handling.
• Build and integrate LLM-enabled applications and tool-using agents where they improve client outcomes, applying RAG, enterprise data access and workflow orchestration where appropriate.
• Establish 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, reviewing generated output and protecting client information.
• Take solutions into production through testing, CI/CD, monitoring and operational documentation, and resolve rollout issues.
• Establish a clear support and ownership handover and support user adoption, measuring results against agreed baselines.
• Lead technical priorities, estimates, dependencies and risks, and communicate trade-offs to stakeholders.
• 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.
• Partner with engagement leadership on overall scope, staffing, project budgets and client commitments.

What you'll need

• 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.
• 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.
• 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 business data or systems.
• Working knowledge of prompt and context design, structured outputs, tool calling and RAG, including when these approaches are appropriate.
• Experience building and evaluating agent-based workflows using an established SDK, framework or platform, including execution state, tool access, limits, retries, error handling and human approvals.
• Ability to test grounding, task completion and failure scenarios.
• Practical experience using at least one AI-assisted development tool, including reviewing generated code, validating changes through tests and protecting client data and credentials.
• Hands-on use of Git, code reviews, automated testing, CI/CD and environment management.
• Experience deploying, monitoring and troubleshooting production solutions.
• Ability to apply data quality, privacy, security and audit requirements in engineering decisions, and to design reusable datasets and consistent business metrics for analytics.
• Experience working directly with users, translating ambiguous problems into practical solutions and iterating on feedback.
• Ability to understand business rules, exceptions and approval requirements, working with functional specialists where needed.
• Experience leading technical delivery and mentoring engineers, including estimating work, managing technical risks and explaining technical choices to business and engineering audiences.
• A bachelor's degree or an equivalent combination of relevant education and professional experience.

Nice to have

• Experience delivering solutions for the Office of the CFO, particularly in at least one area such as financial close and reconciliation, management reporting, FP&A and variance analysis, or working capital; familiarity with relevant data, business rules, controls and approval workflows is more important than breadth across all finance functions.
• Relevant Snowflake certification, such as SnowPro Core or an advanced architecture or data engineering certification; experience with Snowpark, Cortex, Streamlit, Native Apps or Apache Iceberg.
• Experience deploying, monitoring and supporting AI applications or agents in production, including retrieval quality, evaluation datasets and continuous improvement based on user feedback.
• Experience developing multi-agent workflows, reusable agent skills or tools, custom agent harnesses, or integrations using Model Context Protocol (MCP), and ability to explain when the additional complexity is justified.
• Experience introducing agent-assisted engineering practices into a delivery team, including reusable project context, automated test generation and review workflows, with evidence of improvements in delivery speed or quality.
• Delivery experience with a BI or analytics platform such as Power BI, Sigma, Tableau, Looker or Omni, including semantic models and governed metrics.
• Experience with integration or orchestration platforms such as Fivetran, Matillion, Airflow, Boomi, MuleSoft or Workato; or interoperability with Microsoft Fabric.
• Experience with AWS, Azure or GCP application hosting, containers, serverless services or infrastructure as code.
• Exposure to ERP, CRM, operations or other enterprise workflows, and to the governance, service management or compliance requirements relevant to those environments.