Solutions Architect
This customer-facing presales role partners with sales teams to design, demonstrate, and validate scalable data and AI solutions. It requires hands-on Python and data-platform experience, architectural judgment, and the ability to communicate with both technical and business stakeholders.
About the job
Responsibilities
- Partner with Account Executives to drive technical sales cycles and customer outcomes.
- Lead customer workshops, discovery sessions, and technical deep-dives across multiple stakeholders.
- Build and deliver proof-of-concepts, demos, and solution prototypes using real data.
- Translate business requirements into practical, scalable data and AI solutions.
- Act as a trusted advisor while working hands-on to validate solutions end-to-end.
- Work through technical challenges with customers, iterating and resolving issues as they arise.
- Contribute reusable assets, patterns, and best practices that accelerate adoption across customers.
Requirements
Customer-facing mindset
- Experience working with customers in a presales, consulting, or advisory capacity.
- Ability to communicate complex topics to technical and non-technical audiences.
- Comfortable leading discussions that connect business goals to technical decisions.
Hands-on technical foundation
- Experience with Python or a similar language.
- Familiarity with distributed data processing, compute, and large-scale datasets.
- Understanding of data pipelines and modern data architectures.
- Ability to work practically with data, including building proof-of-concepts, exploring datasets, and troubleshooting.
- Comfortable reasoning through code, explaining how it works, and adapting it when needed.
Data and AI understanding
- Familiarity with analytics, data science, or machine learning workflows.
- Understanding of how data platforms support AI use cases, including feature pipelines, model training, and inference.
- Interest in modern AI capabilities and how they operate on real data systems.
Architecture and platform understanding
- Familiarity with cloud platforms and data technologies.
- Ability to design solutions that consider scalability, performance, reliability, and data volumes.
- Awareness of architectural trade-offs and ability to adapt designs based on constraints.
Preferred qualities
- Comfortable switching between business dialogue and technical execution.
- Able to build, validate, and iterate on solutions in practice.
- Takes ownership of technical challenges and pushes problems forward.
- Applies structured thinking in discovery and connects requirements to scalable technical solutions.
- Understands that AI solutions depend on solid data foundations.
- Curious and committed to continuously deepening technical expertise.
Compensation and benefits
- Comprehensive benefits and perks are offered, with details varying by region.
Skills
Python, Distributed Data Processing, Data Pipelines, Data Architecture, Machine Learning, Data Science, Analytics, Feature Pipelines, Model Training, Inference, Cloud Platforms, Proofs Of Concept
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