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Delivery Solutions Architect - Public Sector (DOJ) at Databricks - ApplyBlast

Delivery Solutions Architect - Public Sector (DOJ)

Databricks

11 days ago

4.1 on Glassdoor

Huntsville, Alabama, United States

Hybrid

Full Time

Senior

0 applicants

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About this role

The Delivery Solutions Architect will serve as the post-sale technical lead for strategic public sector accounts, driving customer success and accelerating the adoption of the Databricks Data Intelligence Platform. This hybrid technical and commercial role involves collaborating with sales and field engineering teams to maximize customer ROI, resolve complex technical issues, and execute strategic roadmaps for data and AI workloads. The position requires strong technical expertise in data systems, program management skills, and the ability to build executive relationships within the greater Huntsville, Alabama region.

Responsibilities

Lead post-sale technical account strategy and execution for strategic customer use cases
Serve as primary technical lead for production and go-live status of customer workloads
Drive customer success by accelerating platform adoption and maximizing return on investment

Skills

Qualifications

Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent experience through work experienceTop Secret Clearance

About Databricks

databricks.com

Headquarters

San Francisco, California, United States

Company Size

5001-10000 employees

Founded

2013

View Profile

Databricks is a unified Data Intelligence Platform that helps organizations build, deploy, and manage data, analytics, and AI at scale. Built on Apache Spark and Delta Lake, it provides a “lakehouse” architecture that combines data engineering, ETL, data warehousing, streaming, and machine learning (including MLflow and feature store) in a single managed service. The platform includes collaborative notebooks, scalable compute across major clouds, and built‑in governance, security, and performance optimizations. Enterprises use Databricks to simplify data pipelines, accelerate model development with a data‑centric approach, and operationalize analytics and AI.