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Sr. Forward Deployed Engineer

AI Summary
Databricks seeks a senior Forward Deployed Engineer to deliver production-grade data, AI, and application solutions for enterprise customers. The role owns architecture and technical design, builds end-to-end implementations, and helps customers adopt the Databricks platform. It combines hands-on engineering in Spark, cloud platforms, MLOps, and modern programming languages with client-facing delivery and stakeholder management. The engineer will create reusable accelerators, collaborate with product and support teams, and travel to customer sites up to 20% of the time.
Remote from
USA
Salary
USD 182k–250,208 / yr
Employment
Full Time
Experience
Senior
Published
Apply before
5 Dec 2026
Listing views
14
Application actions
1
Application toolkit

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Opportunity details

About this role.

CSQ427R94

As a Forward Deployed Engineer (FDE) you will work with customers to build and productionize solutions to their data & AI challenges using the Databricks platform. You will own the architecture, lead design decisions, and implement end-to-end systems spanning data engineering, AI, and application development. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations. FDEs deliver with customer empathy, integrating with client systems, training, and other technical needs to help customers get most value out of their data.

This is a hands-on, customer-facing role for builders who thrive at the intersection of technology and business impact. The ideal candidate combines engineering expertise with adaptability, curiosity, and a passion for working with customers and teammates to solve complex problems that drive measurable outcomes. FDEs are billable and know how to complete projects according to specification with exceptional customer empathy.

The impact you will have:

  • Production Solution Delivery: Lead impactful customer technical projects by delivering production-grade systems, designing and building reference architectures, custom applications and data ingestion and ML/AI model integration
  • Transformational Impact: Guide strategic customers as they implement transformational big data projects including end-to-end design, build and deployment of industry-leading big data and AI applications. Work with engagement managers to scope technical delivery work with input from the customer
  • Empower Customers: Guide customers on architecture and design; bootstrap or implement customer projects which leads to a customers’ successful understanding, evaluation and adoption of Databricks.
  • Own the Architecture: Lead architecture and design decisions, ensuring solutions are secure, scalable, and aligned with both customer needs and Databricks best practices.
  • Work with the Databricks technical team, Project Manager, Architect and Customer team to ensure the technical components of the engagement are delivered to meet customer’s needs.
  • Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution for engagement specific product and support issues.
  • Customer Immersion: Embed with customer teams, engaging with stakeholders from technical ICs to executives to deeply understand challenges and deliver impact.
  • Reusable Assets & Scale: Contribute accelerators, frameworks, and best practices that scale impact across accounts and influence the Databricks product roadmap.

What we look for:

  • 6+ years experience in data engineering, data platforms & analytics, or software engineering
  • Comfortable writing code in either Python, Scala, JavaScript/TypeScript, and modern frameworks
  • Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one
  • Deep experience with distributed computing with Apache Spark™ and knowledge of Spark runtime internals
  • Familiarity with CI/CD for production deployments
  • Working knowledge of MLOps, ML/AI models and AI APIs
  • Design and deployment of performant production end-to-end data architectures and applications that combine data pipelines, ML/AI models, and user-facing interfaces.
  • Experience with technical project delivery – managing scope, timelines and measurable outcomes, translating complex concepts into actionable solutions.
  • Documentation and white-boarding skills.
  • Experience working with enterprise clients and managing conflicts across a broad stakeholder range
  • Build skills in technical areas, and demonstrate curiosity, adaptability, and eagerness to explore new technologies which support the deployment and integration of Databricks-based solutions to complete customer projects.
  • Travel to customers 20% of the time
  • Databricks Certification

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 base 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 anticipated 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.

Zone 1 Pay Range

$182,000—$250,208 USD

Zone 2 Pay Range

$182,000—$250,208 USD

Zone 3 Pay Range

$182,000—$250,208 USD

Zone 4 Pay Range

$182,000—$250,208 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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This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThis is a senior, customer-embedded engineering role requiring deep distributed-systems expertise plus ownership of production delivery across data, AI, and application layers. Success depends on independently resolving ambiguous technical and stakeholder challenges while delivering measurable outcomes.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$216,104
US market range (per year)$180k–$260k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses a USD 182,000–250,208 base-pay range. The annual cadence is inferred from the magnitude and senior US engineering context because the posting does not explicitly state a pay period. Estimated US market pay for a senior customer-facing data and AI engineer is USD 180,000–260,000 per year, excluding bonus and equity.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you design a production data and AI solution for an enterprise customer on Databricks?

I would begin with business outcomes, data sources, security constraints, and service-level requirements. I would then design ingestion, transformation, governance, model lifecycle, serving, observability, and CI/CD components, validating the architecture with stakeholders before delivering an incremental production rollout.

Describe how you diagnose and improve an underperforming Apache Spark workload.

I first inspect the query plan, Spark UI, partitioning, skew, shuffle volume, storage format, and cluster configuration. I then prioritize changes such as predicate pushdown, efficient file layouts, partition sizing, join strategy adjustments, skew mitigation, and caching only where it provides measurable value.

How do you manage scope and expectations during a customer-facing technical engagement?

I establish measurable deliverables, assumptions, decision owners, milestones, and risks at the start of the engagement. I communicate trade-offs early, document changes, and use regular technical checkpoints to keep the customer and internal team aligned on outcomes.

What practices would you use to productionize an ML or AI model?

I would implement versioned data and model artifacts, automated testing, deployment pipelines, access controls, monitoring for quality and latency, and a rollback strategy. I would also define evaluation criteria and feedback loops so the model can be safely improved after release.

How do you communicate a complex architecture decision to both engineers and executives?

For engineers, I provide diagrams, interfaces, constraints, and implementation trade-offs. For executives, I focus on business impact, risk, cost, timeline, and the decision required, while ensuring both audiences understand how the approach supports the desired outcome.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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