# Solutions Architect

**Company:** [Databricks](https://hotfix.jobs/companies/databricks)
**Location:** Stockholm, Sweden
**Role:** Solutions Architecture
**Skills:** Python, Distributed Data Processing, Data Pipelines, Data Architecture, Machine Learning, Data Science, Analytics, Feature Pipelines, Model Training, Inference, Cloud Platforms, Proofs Of Concept
**Posted:** 2026-08-11

> 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.

## Job Description

## 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.

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