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Sr. Forward Deployed Engineer – Public Sector

AI Summary
Databricks is hiring a senior Forward Deployed Engineer to deliver production data and AI solutions for federal, state, and local government customers. The role owns technical architecture and end-to-end implementation across data engineering, ML/AI integration, cloud platforms, and user-facing applications. It is a customer-facing delivery position requiring stakeholder management, technical project execution, and approximately 25% travel to client sites. Candidates need substantial Apache Spark and cloud experience, along with programming fluency in Python, Scala, and JavaScript or TypeScript. Eligibility for a U.S. government Public Trust, Secret, or Top Secret clearance is required because of client access obligations.
Remote from
USA
Salary
USD 182k–250,208 / yr
Employment
Full Time
Experience
Senior
Published
Apply before
7 Dec 2026
Listing views
38
Application actions
2
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Opportunity details

About this role.

CSQ327R221

PLEASE NOTE:
Due to federal contract requirements and client site access obligations, eligibility for a U.S. government Public Trust, Secret and/or Top Secret clearance are required to access classified information. Candidates with the interest and eligibility to obtain an active Public Trust or higher clearance are strongly encouraged to apply.

This role requires 25% travel to client sites.

About Databricks

At Databricks, we are passionate about enabling data teams to solve the world’s toughest problems, from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world’s best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers, and customer obsessed, we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we’re only getting started.

The Role

As a Sr. Forward Deployed Engineer (FDE), you will work with federal, state, and local government 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.
  • Deliver Technical Components: 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
  • Cross-Collaborate: Work with Engineers and 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’re Looking For

Minimum Qualifications

  • 5+ years experience in data engineering, data platforms & analytics, or software engineering
  • Fluency in writing code in 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, documentation and white-boarding skills
  • Experience working with enterprise clients and managing conflicts across a broad stakeholder range

    Preferred Qualifications

  • 8+ years experience in data engineering, data platforms & analytics, or software engineering
  • Databricks Certification
  • Expertise in MLOps, ML/AI models, and AI APIs

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

4/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightЭто сложная senior-роль, сочетающая глубокую инженерную экспертизу в распределённых системах и AI с самостоятельной поставкой решений для государственных заказчиков. Необходимо принимать архитектурные решения, работать с широким кругом стейкхолдеров и учитывать требования допуска к защищённой информации.

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. Указан ожидаемый годовой диапазон базовой оплаты $182,000–$250,208 USD; период годовой обоснован формулировкой о base salary и уровнем сумм для senior-инженерной роли в США. Оценочный рыночный диапазон для senior customer-facing data/AI engineering роли в США составляет $180,000–$260,000 USD в год без учёта бонусов и equity.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
Describe an end-to-end data and AI solution you delivered into production.

I would explain the business problem, target architecture, ingestion and transformation pipelines, model or AI API integration, deployment process, observability, and measurable outcome. I would also cover security controls, performance trade-offs, and how I enabled the customer team to operate the solution.

How do you diagnose and optimize a poorly performing Apache Spark workload?

I start with Spark UI and query plans to identify skew, expensive shuffles, inefficient joins, partition imbalance, serialization overhead, and input-file issues. I then test targeted changes such as partition tuning, broadcast joins, predicate pushdown, file compaction, caching where justified, and resource configuration, validating improvements with representative workloads.

How would you design a secure data and AI architecture for a public-sector customer?

I would begin with data classification, identity and access requirements, network boundaries, audit obligations, and the customer’s authorization process. The design would apply least-privilege access, encryption, governed data catalogs, isolated environments, logging, CI/CD controls, and documented operational procedures while balancing security with delivery needs.

How do you manage conflicting priorities between customer stakeholders during a technical engagement?

I make the competing goals explicit, connect each request to the agreed business outcomes, and assess scope, risks, dependencies, and delivery impact. I facilitate a decision with stakeholders, document the trade-off and ownership, and maintain a transparent prioritized plan rather than allowing unapproved scope expansion.

What approach would you take to productionize an ML or generative-AI application?

I would establish reproducible data pipelines, versioned code and models, automated testing, deployment controls, monitoring for quality and drift, access safeguards, and a rollback path. For generative AI, I would additionally evaluate prompt and retrieval quality, guardrails, sensitive-data handling, latency, and human feedback loops.

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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