Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Sr. Solutions Architect – Travel and Hospitality
- Remote from
- USA
- Salary
- USD 219,100–301,300 / yr
- Department
- Software Engineering
- Employment
- Full Time
- Experience
- Director
- Published
- Apply before
- 7 Dec 2026
- Listing views
- 43
- Application actions
- 2
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About this role.
Sr. Solutions Architect (L6)
The Role
As a Sr. Solutions Architect, you are an expert-level technical leader responsible for the organization’s most complex customer engagements. You drive consumption strategy, own executive-level relationships, and serve as the definitive technical authority for your accounts. You have declared your archetype—a recognized technical specialization that makes you a force multiplier across your team and region. You operate at the intersection of deep technical expertise, strategic account planning, and executive influence.
The Impact You Will Have
- Drive the technical and consumption strategy for the organization’s most complex customer engagements, directly influencing platform adoption and revenue growth.
- Own senior-level technical relationships — serving as a trusted advisor on enterprise data and AI strategy
- Lead the most complex architecture engagements: multi-workload platforms, enterprise-wide migrations, real-time systems, and AI/ML at scale
- Operate as an Expert on the Databricks Platform with a declared archetype — actively mentoring others and driving horizontal impact across your team
- Develop and execute competitive strategies for your customers, positioning Databricks against incumbent and emerging platforms
- Orchestrate cross-functional teams (DSAs, SSAs, Partners, Product) to deliver enterprise-grade solutions
- Coach and develop junior SAs (L3–L5) on technical depth, customer engagement, and use case prioritization
- Contribute to Field Engineering thought leadership through customer-facing content, workshops, and community engagement
What We Look For
- 8+ years in solutions architecture, principal engineering, technical pre-sales leadership, or a senior customer-facing technical role
- Expert-level coding proficiency — Python, SQL, and ideally Spark/PySpark. You will complete a live coding assessment and a platform build during the interview
- Deep expertise in enterprise data architecture: distributed systems at scale, streaming/real-time, lakehouse design patterns, data governance frameworks, and cloud-native platforms
- Expert-level Databricks Platform knowledge (or equivalent depth in competing platforms with demonstrated rapid learning ability)
- Declared technical specialization with recognized depth — e.g., real-time architectures, ML/AI platforms, data governance, large-scale migrations, or industry-specific solutions
- Proven track record of driving consumption growth and platform adoption in large accounts
- Executive presence — ability to influence Directors, VPs, and C-level stakeholders on long-term data and AI strategy
- Experience leading competitive displacements and complex enterprise evaluations
- Strong strategic thinking — connecting technical architecture decisions to business outcomes and revenue impact
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a quantitative discipline (or equivalent experience); advanced degrees are valued
Nice to Have:
- Databricks certifications
- Prior experience at Databricks, Snowflake, AWS, Google, Microsoft, or a top-tier data/AI company
- Published thought leadership (blogs, conference talks, open source contributions)
- Industry vertical expertise with executive-level domain credibility
Interview Process: Recruiter Screen → Hiring Manager Screen → Design and Architecture Interview → Live Coding Assessment → Build, Demo, Pitch! Presentation → Reference Check
Pay Range Transparency
Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.
Local Pay Range
$219,100—$301,300 USD
About Databricks
Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn, X, YouTube, and Instagram.
Benefits
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here.
Our Commitment to Diversity and Inclusion
At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.
Compliance
If access to export-controlled technology or source code is required for performance of job duties, it is within Employer’s discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.
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Sample interview questions
I would start by defining priority business domains, latency requirements, data ownership, and regulatory constraints. I would propose a layered lakehouse architecture with streaming ingestion, governed bronze/silver/gold data products, centralized catalog and lineage controls, and workload isolation for BI, data engineering, and ML. I would also establish observability, cost controls, disaster recovery, and a phased migration plan tied to measurable adoption outcomes.
I would explain the customer’s technical and commercial constraints, map the incumbent’s weaknesses to high-value use cases, and build a proof of value around measurable outcomes such as lower pipeline latency, reduced operating cost, or faster model deployment. I would engage technical champions and executive sponsors, document migration risks, and create an incremental roadmap that reduced switching risk. Success should be demonstrated through adoption metrics and a durable expansion plan rather than a one-time evaluation win.
I assess business latency needs, event volume, ordering and delivery guarantees, data quality requirements, operational complexity, and total cost of ownership. Streaming is appropriate when decisions or customer experiences require low-latency data, while batch is often better for periodic reporting and cost-efficient backfills. In many enterprise designs, I use a hybrid approach with streaming for critical events and batch reconciliation for completeness and governance.
I begin with the business objective, financial impact, risk reduction, and decision required rather than implementation details. I use a concise architecture narrative that links platform capabilities to outcomes such as faster time to insight, improved personalization, or stronger governance. I reserve deep technical detail for architects and ensure executives leave with clear trade-offs, success metrics, ownership, and next steps.
I would practice writing clean, testable Python and efficient SQL under time constraints, with particular attention to joins, aggregations, window functions, and data-quality handling. For Spark, I would demonstrate knowledge of partitioning, avoiding unnecessary shuffles, selecting appropriate join strategies, and explaining performance trade-offs. During the exercise, I would clarify assumptions, narrate my reasoning, validate edge cases, and discuss how I would productionize the solution.
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