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Delivery Solutions Architect
Summary by AIEngineer.careers
Delivery Solutions Architect is a AI Solutions Architect role (full-time). with Databricks. in STOCKHOLM, SE. Imported listing (source: aihiringboard.com). Apply on the employer's site (aihiringboard.com).
Imported listing (source: aihiringboard.com) · Apply on aihiringboard.com
STOCKHOLM · SE
—
Full-time
SENIOR
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from aihiringboard.com
Databricks is seeking a Delivery Solutions Architect to serve as a trusted technical advisor for key customers in Sweden, Denmark, and Finland. This hybrid role combines technical architecture expertise with commercial account strategy, helping customers design, implement, and scale data and AI solutions on the Databricks Data Intelligence Platform. You will lead post-sales technical engagements, drive adoption of Databricks products, and orchestrate cross-functional teams to accelerate customer success.
Responsibilities
- Act as the Databricks Architect for customer technical teams, guiding use cases from development to go-live and resolving technical challenges.
- Lead post-sales technical account strategy and execution for strategic accounts.
- Serve as internal point of contact for production status of agreed use cases across multiple accounts.
- Leverage shared services, education, and support resources to address tasks beyond your expertise.
- Create and execute plans to accelerate key use cases into production, coordinating with Professional Services.
- Navigate Databricks Product and Engineering teams for new innovations, private previews, and upgrade needs.
- Report on usage, customer health, risks, blockers, product adoption, and use case progression to Field Engineering leadership.
Requirements
Databricks
San Francisco · US · 16244+ employees
Databricks is a data and AI company that created the data lakehouse architecture. Its unified platform supports data engineering, data warehousing, machine learning, and AI application development.
