Solutions Architect - CPG and Manufacturing
About this role
Technical strategy discussions and production-grade solution design occupy the daily schedule for this role, connecting data foundations to artificial intelligence outcomes for CPG and manufacturing accounts. The engineering team builds data and AI architectures, utilizing tools like the Databricks Platform to drive platform adoption and consumption growth from discovery through production deployment.
Account ownership spans the entire technical journey, requiring collaboration with customer architects, engineering leads, and directors while coordinating cross-functional resources. Candidates who succeed here combine hands-on technical depth in domains such as machine learning or agentic systems with the ability to translate complex technical architectures into business value for senior stakeholders.
What you will do
- Own the end-to-end technical strategy for assigned accounts from discovery to production deployment.
- Lead complex architecture discussions covering data engineering, machine learning, agentic systems, and real-time analytics.
- Act as a trusted technical partner to customer architects, engineering leads, and directors.
- Develop a technical specialization and serve as a go-to resource within the team for domain depth.
- Orchestrate cross-functional resources including data specialists, solution specialists, and partners to deliver comprehensive solutions.
- Provide structured feedback regarding customer requirements and competitive gaps directly to product teams.
What you need
- Bring six or more years of experience in solutions architecture, data engineering, data science, artificial intelligence, or technical pre-sales.
- Demonstrate deep expertise in modern data and AI architectures such as lakehouse design, scalable pipelines, streaming, and cloud-native platforms.
- Possess a strong understanding of artificial intelligence and machine learning concepts, agentic architectures, and ontology design.
- Show proficiency on the Databricks Platform or a demonstrated ability to achieve proficiency rapidly.
- Hold experience with production deployments on public cloud environments like AWS, Azure, or GCP, including security and governance considerations.
- Possess a bachelor's or master's degree in Computer Science, Engineering, or a quantitative discipline, or equivalent practical experience.
Nice to have
- Databricks certifications including Data Engineer, ML Associate or Professional, and Platform
- Experience with competitive platforms such as Snowflake, AWS native services, or Azure Synapse
- Background in an AI-first company, data platform vendor, or cloud provider
- Industry domain expertise, preferably in CPG and Manufacturing
Skills and tech stack
Questions candidates ask
What is the focus of the technical specialization for this position?
The role involves developing an emerging technical specialization, ideally in artificial intelligence, machine learning, agentic architectures, or a related domain. Professionals become go-to resources within the team for depth in their specific focus area.
What platforms and technologies will I work with?
Team members work primarily on the Databricks Platform, alongside public cloud environments such as AWS, Azure, and GCP. The work touches lakehouse design, streaming data pipelines, compound AI systems, and autonomous agent frameworks.
Who are the primary stakeholders I will interact with?
Architects, engineering leads, and directors at customer organizations serve as the main points of contact. The job also requires close collaboration with internal product teams and cross-functional resources like partners and specialists.