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Specialist Solutions Architect - Data Engineering & Observability at Databricks - ApplyBlast

Specialist Solutions Architect - Data Engineering & Observability

Databricks

4 months ago

4.1 on Glassdoor

United States

Onsite

Full Time

Senior

0 applicants

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About this role

As a Specialist Solutions Architect in Data Engineering and Observability, you will guide customers through cloud data transformations and align their technical roadmaps with the Databricks Data Intelligence Platform. You will provide architectural leadership for large-scale warehousing and pipeline projects while collaborating with sales teams to deliver custom proofs of concept and technical training. This role demands deep expertise in big data technologies to drive successful implementations and community engagement for the platform.

Responsibilities

Architect and optimize large-scale production data pipelines
Guide strategic customers through cloud data engineering transformations
Design cost-efficient high-performance data workloads in cloud environments
Support Solution Architects with complex technical sales and PoCs
Develop training materials and present at conferences for community adoption

Skills

Qualifications

Bachelor's degree in Computer Science5+ years of experience in Data Engineering

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.