Solutions Architect - Field Engineering (gcc)

Databricks Databricks · Data AI · Delhi, India · Field Engineering - Other

Solutions Architect for Databricks' Field Engineering team, focusing on advising large enterprise and global AI-forward SaaS customers on leveraging the Databricks Data Intelligence Platform for data engineering, analytics, and AI/ML, including GenAI. The role involves designing architectures, implementing proofs of concept, and driving customer adoption of Databricks solutions.

What you'd actually do

  1. Be the trusted technical advisor for one or more named strategic accounts — building deep, durable relationships with their data, AI/ML, and platform engineering leaders, and earning a seat at the table for their most important architectural decisions.
  2. Help shape and drive your customer's AI strategy — partnering with their data science, ML, and AI platform teams to design GenAI and ML architectures, evaluate build-vs-buy decisions, and turn AI ambitions into production systems deployed on Databricks.
  3. Partner with the sales team and provide hands-on technical leadership to help large enterprise customers understand how Databricks can help solve their business problems across the full data and AI lifecycle.
  4. Consult on modern Lakehouse architectures and implement proofs of concept for strategic projects spanning data engineering, data warehousing, machine learning, GenAI— including validating integrations with cloud services, in-house tools, and third-party applications.
  5. Work directly with the sales team to develop your book of business, define account strategies, and execute those strategies to help your customers and prospects solve their business problems with Databricks.

Skills

Required

  • Python
  • SQL
  • Scala
  • Java
  • R
  • Big Data technologies
  • Apache Spark
  • data engineering
  • data science
  • modern AI/ML workloads
  • architectural experience
  • distributed data systems
  • AWS
  • Azure
  • GCP

Nice to have

  • Databricks Certification

What the JD emphasized

  • 12+ years in data engineering, data science, technical architecture, or a similar pre-sales / consulting role.
  • 8+ years hands-on experience with Big Data and AI technologies, including Apache Spark™, data engineering, data science, and modern AI/ML workloads.
  • Strong hands-on architectural experience — able to whiteboard, design, and personally build end-to-end solutions, not just advise.

Other signals

  • customer-facing technical advisor
  • design GenAI and ML architectures
  • turn AI ambitions into production systems
  • implement proofs of concept
  • build end-to-end solutions