Senior Solutions Engineer (Manufacturing, Automotive, Defence)

Overview

In this role you will independently lead technical engagements, from discovery to design and platform demonstrations, to drive Databricks adoption. You’ll own frontline customer relationships and work with Account Executives to develop technical strategies that expand platform usage. You will build proofs-of-concept and live demos, tackling data engineering, analytics, and ML workloads at scale. You help shape customer outcomes by articulating differentiation and contributing reusable technical assets to the SA community. This is a hands-on, customer-facing role that combines coding, architecture, and presentation to deliver measurable impact.

Responsibilities

• Lead technical discovery and solution design for customer workloads (data engineering, analytics, ML)
• Build and deliver proofs-of-concept and live platform demos to drive technical wins
• Own frontline technical relationships with customer engineers and data teams
• Develop account-level technical strategies with Account Executives to grow platform consumption
• Navigate competitive landscapes by showcasing Databricks differentiation through demonstrations
• Contribute reusable technical assets (notebooks, accelerators, reference architectures) to the SA community

Key requirements

• 4+ years in data engineering, solutions architecture, technical pre-sales, or hands-on consulting
• Proficient in Python and PySpark/Spark; able to debug, optimize, and produce production-quality code (live coding in interview)
• Hands-on experience designing data solutions on AWS, Azure, or GCP
• Working knowledge of distributed data systems (Apache Spark, Delta Lake, Hadoop, Kafka, Flink)
• Experience leading technical customer conversations (discovery, whiteboarding, architecture reviews)
• Familiarity with data engineering, data science/ML, or SQL analytics
• Strong presentation and demo skills; able to build and present live solutions during interviews
• Bachelor’s or Master’s degree in CS, Engineering, or a quantitative discipline (or equivalent)

• Excellent communication and presentation
• Customer-facing collaboration
• Strategic thinking and stakeholder management
• Python
• PySpark/Spark
• Apache Spark

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