Senior AI Data Architect / Solution Engineer

We are looking for a Senior AI Data Architect / Solution Engineer to design the architecture that delivers trusted data at scale — and to build the AI products that put it directly in the hands of the business. This is a hands-on, senior role for someone who can set the technical blueprint, build the agents on top of it, and act as the bridge between business needs and technical reality.

You will work closely with the Director and Enterprise Architect, serve as technical lead and advisor to the team, and partner with the Senior AI Data Engineer to take designs from blueprint to production. Success is measured by the trust, scale, and adoption of the data and AI products that ship — and by the business outcomes they unlock.

Here is how, through this role, you will contribute to BMC's and your own success:

•Architecture for Scale (Primary Focus)— Design the reference architecture and delivery patterns that take governed data from the semantic layer to every downstream consumer — dashboards, applications, and AI agents — reliably and at scale, with the build-vs-buy calls and standards that let the platform grow without painful rework.

•AI Data Products & Agents— Architect and build the Data Agent, the natural-language interface that lets business users query governed data without writing SQL; design agentic workflows with orchestration, tool/function calling, retrieval, evaluation, and guardrails.

•Semantic Foundation— Co-design the semantic layer (HoneyDew / DBT) — the metrics, dimensions, and governed business meaning that power both analytics and AI — ensuring agents query governed definitions, not raw tables.

•Technical Leadership— Serve as technical lead and trusted advisor; set patterns, run design reviews, and mentor engineers, partnering with the Data Engineer to take designs to production.

•Business Translation— Translate ambiguous business problems into clear technical requirements; own stakeholder relationships and move fluently between executive framing and engineering detail.

•Ownership & Execution— Take features and subsystems end-to-end, proactively identifying gaps and driving work forward even when requirements are incomplete or evolving.

To ensure you're set up for success, you will bring the following skillset & experience:

•8+ years across data architecture and hands-on engineering, including production systems that held up under real enterprise use.

•Strong proficiency in Python and SQL, with deep, hands-on experience designing and building on Snowflake and DBT.

•Proven experience designing and building LLM / agentic solutions — RAG, function / tool calling, orchestration (LangChain / LlamaIndex / LangGraph), prompt engineering, and evaluation / observability.

•Deep understanding of the semantic layer as the architectural bridge between source data and business meaning — and why it is foundational to trustworthy AI.

•Demonstrated ability to translate complex technical concepts for non-technical stakeholders and influence architecture decisions across teams.

•Strong communication and the ability to lead through execution in a fast-moving environment.

Broad technical exposure (depth not required in all areas):

•Cloud-native architecture across AWS, Azure, or GCP.

•Vector databases, embeddings, and retrieval evaluation.

•CI/CD and modern engineering practices for data and AI systems.

•Containerization and distributed systems (Docker, Kubernetes, microservices).

Whilst these are nice to have, our team can help you develop in the following skills:

•HoneyDew or other universal/headless semantic layers (HoneyDew, Cube, AtScale, dbt Semantic Layer).

•Snowflake Cortex / Snowflake Intelligence, or production text-to-SQL systems.

•Prior technical-lead or principal-engineer role with mentorship responsibility.

•Background bridging pre-sales / solution engineering and hands-on delivery.