San Francisco, California

Sr. Solutions Architect - AI Natives Business

USD 219.1K–301.3K / yearSeniorDevOps / SRE / platform / cloud

PythonSQLDatabricksSparkMLflowAWSAzureGCPHadoopKafkaPandasscikit-learn

Original posting ↗Apply ↗Company job board ↗Follow new Python jobs in Telegram

Databricks, a software company in AI and data, has an open position for Sr. Solutions Architect - AI Natives Business. The posting gives San Francisco, United States as the location. We classify it as a senior-level role in DevOps / SRE / platform / cloud. The stack in the posting includes Databricks, Spark, MLflow, AWS, Azure and GCP. There are 177 open positions from Databricks on this site, 69 of them in IT and engineering.

Posted today · Last checked 2026-11-05

About Databricks

AI and data Software product company 177 open positions, 39% in IT and engineering. Most open roles: Sales (90), DevOps / SRE / platform / cloud (47), Software engineering (general) (28).

All open jobs at Databricks →

Similar jobs

Senior Infrastructure Engineer (Platform)northwoodspace · Torrance, CAUSD 145K–190K / year Software Integration Engineer 4 - Linux/Bash/Python/Docker/Kubernetes/HelmCaptivation Software · Annapolis Junction, MDUSD 130K–270K / year Systems Engineer (5827) (Kirtland AFB, New Mexico) (TS/SCI)SMX · Kirtland AFB, New MexicoUSD 100K–135K / year Site Reliability Engineer (Infrastructure, Virtualization, & Compute)SpaceX · Memphis, TN Founding Cloud Infrastructure EngineerClera · San MateoOn-site Solution ArchitectGusto, Inc. · Atlanta, GA - Hybrid; Denver, CO - HybridUSD 120K–150K / year Senior Systems Engineer GRVTY · Aurora, Colorado, United States; Sterling, Virginia, United StatesUSD 160K–225K / year Senior Solutions ArchitectBuildkite · US - East Coast

1,467 open DevOps / SRE / platform / cloud jobs in the United States All open jobs at Databricks →

Workplace	Workplace not stated
Seniority	Senior
Role	DevOps / SRE / platform / cloud
Countries	United States
Languages	PythonSQL
Key facts	Stack: Python, SQL, Databricks, Spark, MLflow, AWS, Azure, GCP, Hadoop, Kafka, Pandas, scikit-learn

The full description is on the original posting. Labels are set automatically and can be wrong. About the data