databricks
1 month ago
Forward Deployed Engineering - Senior Architect
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Company information
- Company
- databricks
- Location
- London Germany
- Posted
- 1 month ago
Job description
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As a Forward Deployed Engineer (FDE) you will work with customers to build and productionize solutions to their data & AI challenges using the Databricks platform. You will own the architecture, lead design decisions, and implement end-to-end systems spanning data engineering, AI, and application development. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations. FDEs deliver with customer empathy, integrating with client systems, training, and other technical needs to help customers get most value out of their data.
This is a hands-on, customer-facing role for builders who thrive at the intersection of technology and business impact. The ideal candidate combines engineering expertise with adaptability, curiosity, and a passion for working with customers and teammates to solve complex problems that drive measurable outcomes. FDEs are billable and know how to complete projects according to specification with exceptional customer empathy.
The impact you will have:
- Production Solution Delivery: Lead impactful customer technical projects by delivering production-grade systems, designing and building reference architectures, custom applications and data ingestion and ML/AI model integration
- Transformational Impact: Guide strategic customers as they implement transformational big data projects including end-to-end design, build and deployment of industry-leading big data and AI applications. Work with engagement managers to scope technical delivery work with input from the customer
- Empower Customers: Guide customers on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks.
- Own the Architecture: Lead architecture and design decisions, ensuring solutions are secure, scalable, and aligned with both customer needs and Databricks best practices.
- Work with the Databricks technical team, Project Manager, Architect and Customer team to ensure the technical components of the engagement are delivered to meet customer's needs.
- Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution for engagement specific product and support issues.
- Customer Immersion: Embed with customer teams, engaging with stakeholders from technical ICs to executives to deeply understand challenges and deliver impact.
- Reusable Assets & Scale: Contribute accelerators, frameworks, and best practices that scale impact across accounts and influence the Databricks product roadmap.
What we look for:
- 6+ years experience in data engineering, data platforms & analytics, or software engineering
- Comfortable writing code in either Python, Scala, JavaScript/TypeScript, and modern frameworks
- Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one
- Deep experience with distributed computing with Apache Spark™ and knowledge of Spark runtime internals
- Familiarity with CI/CD for production deployments
- Working knowledge of MLOps, ML/AI models and AI APIs
- Design and deployment of performant production end-to-end data architectures and applications that combine data pipelines, ML/AI models, and user-facing interfaces.
- Experience with technical project delivery - managing scope, timelines and measurable outcomes, translating complex concepts into actionable solutions.
- Documentation and white-boarding skills.
- Experience working with enterprise clients and managing conflicts across a broad stakeholder range
- Build skills in technical areas, and demonstrate curiosity, adaptability, and eagerness to explore new technologies which support the deployment and integration of Databricks-based solutions to complete customer projects.
- Travel to customers 20% of the time
- Databricks Certification
About Databricks
Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.
Benefits
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here.
Our Commitment to Diversity and Inclusion
At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.
Compliance
If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.
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Required skills
- databricks
- technology
- travel
- engineering
- training
- compliance
- documentation
- ci/cd
- javascript
- typescript
- ai
- python
- aws
- projects
- technical skills
- software engineering
- inclusive culture
- evaluation
- gcp
- azure
- adaptability
- project manager
- architecture
- hands-on
- best practices
- architect
- delta lake
- accounts
- disability
- analytics
- data engineering
- product
- distributed computing
- frameworks
- data architectures
- data pipelines
- color
- diversity and inclusion
- scala
- deployment
- executives
- stakeholders
- customer-facing
- curiosity
- application development
- accelerators
- customer projects
- cross-functional
- specification
- mlflow
- mlops
- language
- new technologies
- adoption
- scale
- conflict management
- customer needs
- comprehensive benefits
- technical components
- data ingestion
- product feedback
- apache spark
- employees
- equal employment opportunity
- technical delivery
- perks
- ai applications
- engineering expertise
- modern frameworks
- data platforms
- lakehouse
- scalable solutions
- complex problems
- physical ability
- business impact
- client systems
- job duties
- complex concepts
- technical project delivery
- enterprise clients
- reusable assets
- timeline management
- age
- secure solutions
- measurable outcomes
- ai apis
- religion
- reference architectures
- veteran status
- design decisions
- arbeitnow
- protected characteristics
- hiring practices
- strategic priorities
- customer empathy
- billable
- sexual orientation
- race
- scope management
- marital status
- applicant
- deliver impact
- cloud ecosystems
- customer teams
- developer relations
- data intelligence platform
- outcome measurement
- ethnicity
- gender identity
- custom applications
- end-to-end design
- engagement managers
- source code
- strategic customers
- fde
- technical needs
- production deployments
- national origin
- big data projects
- gender expression
- customer team
- data value
- databricks certification
- support issues
- u.s. government license
- forward deployed engineer
- end-to-end systems
- production solution delivery
- customer technical projects
- production-grade systems
- ml/ai model integration
- transformational impact
- big data applications
- customer input
- empower customers
- architecture guidance
- design guidance
- bootstrap projects
- implement customer projects
- successful understanding
- architecture ownership
- databricks best practices
- databricks technical team
- engagement delivery
- databricks customer support
- implementation feedback
- rapid resolution
- engagement specific issues
- product issues
- customer immersion
- technical ics
- understand challenges
- scale impact
- databricks product roadmap
- spark runtime internals
- ml/ai models
- user-facing interfaces
- actionable solutions
- white-boarding
- stakeholder range
- databricks-based solutions
- region specific benefits
- family status
- mental ability
- political affiliation
- socio-economic status
- export-controlled technology
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