Databricks Solutions Architect

• Engage with customers on short- to mid-term professional services projects using the Databricks platform (data engineering, data science, cloud).
• Design and build reference architectures, help with production-level use cases and deployments of big data / AI applications.
• Collaborate with engagement/project managers, customer teams, and internal engineering/support teams to ensure technical delivery meets customer needs
• Provide escalated technical support or operational assistance for customer engagements, helping to resolve issues or unblock customers
• Work hands-on: write code (e.g., Python, Scala), work with distributed compute (Apache Spark), integrate across cloud ecosystems.
• Possibly scope new engagements, support sales or pre-sales (depending on region/team) by estimating effort, defining deliverables.

• Provide feedback to product/engineering from customer engagements (help improve platform, features *, * It s billable professional services: you ll often be working as a consultant/embedded engineer with customers, delivering real implementations rather than purely strategy.

• The role is technically deep: you ll not only design solutions but often build them, debug issues, optimize performance.

Because it is delivery-oriented, you may deal with customer escalations and operational issues, not just green-field new builds.

• You ll need adaptability: customers across industries, various workloads (ETL, streaming, AI/ML), different cloud platforms.

• The role also helps the customer adopt Databricks, so you re enabling change in how they think about data/AI, not just delivering code

• Role of an RSA

• Be a trusted advisor with authority and clarity.
• Set expectations and simplify complex ideas.
• Earn trust early, even before delivering technical work.
• Align with the customer, listen actively, and empathize with their concerns.
• Always under-promise and over-deliver.

Strategic Nature of PS Consulting

• Focus on solving problems to build trust and unlock future work.
• Use clear, jargon-free communication.
• Identify stakeholders, understand drivers of change, and mitigate risks.
• Offer quick-win alternatives to free up customer bandwidth for strategic tasks.

Build vs. Buy Mitigation

• Assess if building aligns with the company s core competencies or adds technical debt.
• Ask: Does owning this make you money, or is it a cost center?
• Highlight Databricks value proposition in scalability and maintainability.
• Empathize custom solutions are often someone s baby.
• Offer bake-offs to demonstrate Databricks strengths.
• Position Databricks as a career-enabler over maintaining fragile systems.

Spark Performance & Business Disruption Concerns

• Educate the customer understand the specifics: data size, current framework, future scaling needs.
• Clarify if Spark is truly the bottleneck.
• Offer bake-offs on speed, performance, and cost.
• Build a clear, phased migration plan with deliverables.
• Coordinate with the account team to align messaging and reinforce trust.

UC Feature Gaps & Overpromising

• Determine if missing features are true blockers.
• Educate the account team on PUPR (Public Preview) vs. GA (General Availability).
• If necessary, build lightweight custom solutions to bridge gaps.
• Re-align customer expectations with reality and roadmap.

• Several years (often 8+ years) in data engineering, analytics, platform roles.
• Strong coding ability in Python or Scala, and comfortable with distributed processing (Spark).
• Experience working across at least one cloud provider (AWS, Azure, Google Cloud Platform) and preferably familiarity with multiple.
• Knowledge of production deployments: CI/CD, MLOps, data architectures.
• Good client-facing / consulting / customer engagement skills (communicating technical ideas, scoping projects, working with customer teams).
• What You Should Expect from the Role, 7+ years experience in Data Eng., data platforms & analytics,

10+ years of consulting experience

• Completed Data Engineering Professional certification & required classes
• Minimum 6-8+ projects delivered with hands-on experience in development on data bricks
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, Google Cloud Platform) with deep expertise in at least one
• Deep experience with distributed computing with Spark with knowledge of Spark runtime internals
• Familiarity with CI/CD for production deployments
• Working knowledge of MLOps
• Current knowledge across the breadth of Databricks product and platform features
• Familiarity with optimizations for performance and scalability