Databricks FDE roles and official interview guidance: Tokyo first

Choose the work you want to own

A Tokyo FDE application needs more precision than “I build AI for customers.” The official postings distinguish billable production implementation, specialized GenAI delivery, adoption strategy, pre-sales technical wins and team leadership. Several adjacent roles also write code. Use the vacancy’s job ID to choose which evidence to prepare; the presence of “FDE” in a title does not establish a shared interview process.

This article records 84 official facts checked on 2026-10-05. A fact ID identifies a statement, not a score or an interview question. The source section, role and region constrain each statement. Table groupings are this article’s own labels; original section names remain in the evidence ledger. The paired practice articles turn these facts into explicitly inferred exercises; they do not reproduce official questions. No candidate anecdotes, leaked exams or candidate achievements are used.

Vacancy identity and observation boundary

All job bodies below were readable on the check date. This means a posting was observed, not that hiring headcount remains available or that an application will still be accepted. Source publication and revision days are unknown. “Senior” and “Manager” are recorded where the title says so; unspecified internal grades stay unknown.

Official role / source	Region	Requisition / ATS ID	Seniority shown
Sr. Forward Deployed Engineer	Tokyo, Japan	CSQ327R52 / 8568173002	Senior IC
Forward Deployed Engineer	Tokyo, Japan	CSQ427R277 / 8741922002	IC; internal level unknown
Senior AI Engineer - FDE (Forward Deployed Engineer)	Tokyo, Japan	FEQ227R196 / 8569392002	Senior IC
Manager, Forward Deployed Engineering	Tokyo, Japan	CSQ427R97 / 8535419002	Manager
Delivery Solutions Architect	Tokyo, Japan	CSQ427R266 / 8813844002	IC; internal level unknown
Sr. Delivery Solutions Architect	Tokyo, Japan	CSQ327R249 / 8583353002	Senior IC
Solutions Architect (Pre-sales) – Manufacturing & Automotive	Tokyo, Japan	FEQ427R141 / 8785053002	IC; internal level unknown
Senior Solutions Engineer (Technical, Presales, Data & AI, DNB)	London, UK	FEQ327R439 / 8613435002	Senior IC
Manager, Forward Deployed Engineering	Sydney, Australia	CSQ327R50 / 8445817002	Manager
Deployment Strategist	India	unknown / 8630011002	Internal level unknown

The service description, speaker biography and interview PDFs are not job openings. The two PDFs carry an April 2025 label in their titles/URLs. That supplies month-level context, not an actual publication day, so their source metadata remains unknown. A 2025 SA guide cannot silently replace a 2026 role-specific posting.

Tokyo FDE: production implementation with a scoped engagement

The scope of DB001–DB015 is Sr. Forward Deployed Engineer, Tokyo, CSQ327R52 / ATS 8568173002. DB016–DB018 belongs to the separate Forward Deployed Engineer posting, CSQ427R277 / ATS 8741922002. The official Sr. body states 8+ years; a mirror’s 10-year value is not used. The two vacancies are not deduplicated merely because their observed requirements overlap.

Fact ID	Evidence grouping	Verified fact
DB001	Posting identity and purpose	Tokyo Sr. FDE posting: CSQ327R52.
DB002	Posting identity and purpose	Build data and AI applications from design through production.
DB003	Posting identity and purpose	Work includes billable customer engagements.
DB004	Day-to-day work	Define scope with Engagement Managers and customer input.
DB005	Day-to-day work	Lead secure, scalable architecture.
DB006	Day-to-day work	Embed with customers; engage ICs through executives.
DB007	Day-to-day work	Troubleshoot with Engineering and Support.
DB008	Day-to-day work	Produce reusable assets and roadmap feedback.
DB009	Experience and skills	Requires 8+ years; official body takes precedence over a 10-year mirror.
DB010	Experience and skills	Know at least two major clouds; specialize in one.
DB011	Experience and skills	Understand Spark distributed computing and runtime internals.
DB012	Experience and skills	Production experience includes CI/CD, MLOps and AI APIs.
DB013	Experience and skills	Languages include Python, Scala and JavaScript/TypeScript.
DB014	Experience and skills	Travel 20%; fluent Japanese and business English.
DB015	Experience and skills	Lists Databricks certifications without specifying a credential type.
DB016	Experience and skills	Tokyo FDE CSQ427R277 also states 8+ years.
DB017	Experience and skills	Production design connects pipelines, models and UI.
DB018	Experience and skills	Handle scope, deadlines, outcomes, documentation and conflict.

Preparation inference: a useful project record should show the agreed technical scope, production path and your own decisions. Keep the cloud boundary, Spark bottleneck, deployment controls and customer handoff visible. Billable participation supports preparing a scope/cost discussion; it does not establish a personal sales quota, utilisation target or permanent on-call obligation.

Tokyo AI FDE: GenAI delivery has a different requirement set

DB019–DB030 is scoped to Senior AI Engineer - FDE (Forward Deployed Engineer), Tokyo, FEQ227R196 / ATS 8569392002. The one-cloud ML requirement and 4–8-week travel interval cannot be replaced by the general FDE’s two-cloud knowledge and 20% travel. “Preferred” distributed-data experience remains preferred.

Fact ID	Evidence grouping	Verified fact
DB019	Posting identity and purpose	Tokyo Senior AI Engineer–FDE: FEQ227R196.
DB020	Posting identity and purpose	Professional Services AI team builds new production applications.
DB021	Day-to-day work	Own production GenAI applications for internal and consumer uses.
DB022	Experience and skills	Experience examples include RAG, multi-agent, Text2SQL and fine-tuning.
DB023	Experience and skills	Evaluate and optimize production GenAI.
DB024	Experience and skills	Production ML experience on one of AWS, Azure or GCP.
DB025	Experience and skills	Quantitative graduate degree or equivalent experience.
DB026	Experience and skills	Teach technical and nontechnical audiences.
DB027	Experience and skills	Databricks/Spark distributed-data experience is preferred.
DB028	Experience and skills	Customer travel as needed, at intervals of 4–8 weeks.
DB029	Experience and skills	Native-level Japanese; business-level English, spoken and written.
DB030	Day-to-day work	Publish technical work, speak at conferences and work with Product/Engineering.

Preparation inference: select evidence that links an application’s intended use to evaluation, optimization and a production decision. RAG, multi-agent, Text2SQL and fine-tuning are experience examples, not a requirement to use all four in one solution. Explain why the simplest approach was sufficient, what failure required a different approach and which comparison you actually ran. Do not substitute a framework list for those decisions.

Tokyo adjacent roles: code does not determine the boundary

Manager: team quality and staffing

DB031–DB035 comes from Manager, Forward Deployed Engineering, Tokyo, CSQ427R97 / ATS 8535419002. Its AI coding-tool expectation is about delivery work. It grants no interview permission.

Fact ID	Evidence grouping	Verified fact
DB031	Day-to-day work	Lead, hire and develop a team of around ten.
DB032	Day-to-day work	Cover discovery, design, implementation, deployment and enablement.
DB033	Experience and skills	Provide design review and technical leadership in production debugging.
DB034	Day-to-day work	Coordinate staffing and engagements with Sales, Field Engineering, Product and PS leaders.
DB035	Experience and skills	Use AI coding tools for productivity, quality and repeatability.

DSA: move a technical win into adoption

DB036–DB042 is Delivery Solutions Architect, CSQ427R266 / ATS 8813844002. DB043–DB046 is Sr. Delivery Solutions Architect, CSQ327R249 / ATS 8583353002. Both are Tokyo postings. Their stated experience levels differ, and the demonstration stage is named Delivery in the first and Pitch in the second. Keep those words when asking the recruiter what to prepare.

Fact ID	Evidence grouping	Verified fact
DB036	Posting identity and purpose	Tokyo DSA connects technical win to production and adoption strategy.
DB037	Day-to-day work	Drive adoption, consumption and measurable business outcomes.
DB038	Day-to-day work	Build working solutions, prototypes and reusable assets.
DB039	Experience and skills	5+ years; build, debug and validate with Python/SQL.
DB040	Experience and skills	Influence without formal authority.
DB041	Selection steps	Recruiter → hiring manager → system design → Vibe Coding → Build, Demo & Delivery → references.
DB042	Working conditions	Up to 30% travel; Japanese required, business English preferred.
DB043	Day-to-day work	Handle high-value projects across departments, regions and products.
DB044	Experience and skills	Tokyo Sr. DSA states 8+ years.
DB045	Selection steps	Names Vibe Coding followed by Build, Demo & Pitch.
DB046	Day-to-day work	Handle cross-system escalations and mentor DSAs.

Industry SA: pre-sales includes implementation

DB047–DB051 belongs to Solutions Architect (Pre-sales) – Manufacturing & Automotive, Tokyo Manufacturing & Automotive, FEQ427R141 / ATS 8785053002. Its coding and prototype responsibilities rule out treating “SA” as sales conversation alone. They still do not establish the FDE’s interview loop.

Fact ID	Evidence grouping	Verified fact
DB047	Posting identity and purpose	Tokyo Manufacturing & Automotive SA is explicitly pre-sales.
DB048	Day-to-day work	Work on discovery, PoC and adoption plans.
DB049	Day-to-day work	Work with AEs on custom demos, prototypes and migration.
DB050	Experience and skills	Python/SQL coding and learning Spark.
DB051	Selection steps	Names system design, Vibe Coding and Build, Demo & Pitch.

Regional evidence and the RSA naming limit

DB052–DB055 applies to Senior Solutions Engineer (Technical, Presales, Data & AI, DNB), London, FEQ327R439 / ATS 8613435002. Mandatory Python/SQL live coding is established for this SE posting. DB056–DB057 applies to Manager, Forward Deployed Engineering, Sydney, CSQ327R50 / ATS 8445817002. Its billable-utilisation metric is not a Tokyo target. DB058 comes from the Alan Reese — Data + AI Summit speaker biography. The FDE/RSA wording coexistence proves only an observed naming difference. Its 2027 header and 2026 session text also prevent an event-date or rename-date conclusion.

Fact ID	Evidence grouping	Verified fact
DB052	Day-to-day work	London Sr. SE covers discovery, architecture, PoC, demos and technical win.
DB053	Experience and skills	4+ years and experience with one public cloud.
DB054	Experience and skills	Specifies mandatory live Python/SQL coding.
DB055	Experience and skills	Requires building and presenting a working solution.
DB056	Day-to-day work	Sydney FDE Manager develops RSAs and AI Engineers; names coexist.
DB057	Day-to-day work	Billable utilisation appears as a regional metric.
DB058	Observed naming	Alan Reese header says FDE; biography says RSA.

DB064–DB069 belongs to the Deployment Strategist posting in India, ATS 8630011002; the requisition and internal grade remain unknown. Its “why/what versus how” division is useful regional evidence, not a standard Japan team diagram. The DS also performs technical work.

Fact ID	Evidence grouping	Verified fact
DB064	Delivery coverage	India DS frames the why/what while FDE handles the how.
DB065	Day-to-day work	Define MVP success criteria, PRD and backlog.
DB066	Delivery coverage	Cover Day 0 readiness and adoption at launch.
DB067	Delivery coverage	Plan maintenance, documentation, training and next projects.
DB068	Experience and skills	DS also reviews code, prototypes and troubleshoots.
DB069	Experience and skills	Often 25–50% or more of time onsite with customers.

Service scope: follow the deliverable across the engagement

DB059–DB063 is the global customer-facing Databricks Forward Deployed Engineering service page. It describes a service, not eligibility for the Tokyo job.

Fact ID	Evidence grouping	Verified fact
DB059	Delivery coverage	Service spans workspace onboarding through a DataOps center of excellence.
DB060	Delivery coverage	Custom services use a statement of work (SOW).
DB061	Delivery coverage	FDEs, AI FDEs and partners can co-deliver when needed.
DB062	Delivery coverage	Implementation includes production planning, prototypes, scalable design and DevOps.
DB063	Delivery coverage	CoE builds shared standards and frameworks across teams.

1. 1Customer goal + agreed scope
2. 2Production plan and architecture
3. 3Implementation and validation
4. 4Customer handoff

1. 1Field implementation
2. 2Reusable assets and standards
3. 3Other teams

1. 1Customer constraint
2. 2Engagement scope discussion
3. 3Revised production plan

This is an original conceptual reading aid, derived from DB004–DB008 and DB060–DB063. Arrows mean a preparation flow, not Databricks’ internal organization or a prescribed hiring exercise. The diagram makes two outputs visible: a customer deliverable and knowledge others can reuse. A project answer should distinguish who accepted the deliverable from who maintains it afterward.

Official interview guidance: retain the target role

DB070–DB076 is company-wide Interviewing With Us guidance. These estimates help candidates plan time; they do not fill the missing Tokyo FDE loop.

Fact ID	Evidence grouping	Verified fact
DB070	Candidate planning	General process often lasts 2–3 months, with variation.
DB071	Candidate planning	General onsite stage often has 4–6 interviews; not a fixed FDE count.
DB072	Candidate planning	Presentations are required for some roles.
DB073	Candidate planning	Usually virtual through Google Meet; other instructions can apply.
DB074	Candidate preparation	Behavioral guidance favors real experience, collaboration, learning and judgment.
DB075	Candidate planning	Aims to give feedback within 48 hours after the final interview.
DB076	Candidate preparation	Do not share former-employer secrets or proprietary materials.

DB077–DB081 is the Solutions Architect Field Engineering Careers Site Interview Prep April 2025 (April 2025 label). DB082–DB083 is the Engineering Careers Site Interview Prep April 2025 for specified SWE/manager roles. DB084 is the Engineering at Databricks page. Neither PDF supplies a current Tokyo FDE hiring-loop guarantee.

Fact ID	Evidence grouping	Verified fact
DB077	Guide target and content	Guide explicitly addresses Solutions Architects, not FDEs.
DB078	Guide target and content	SA overview covers screen, assignment, panel/presentation and references.
DB079	Guide target and content	SA screen covers data architecture, production, cloud and customer interaction.
DB080	Guide target and content	Assignment mentions Python/Scala DataFrame operations.
DB081	Guide target and content	Presentation uses a hypothetical customer to connect questioning with appropriate value.
DB082	Guide target and content	Guide covers Back-End, Front-End/Full-Stack, Database SWE and Engineering Managers.
DB083	Guide target and content	Typical Back-End panel covers coding, algorithms and systems programming.
DB084	Process scope	Engineering recruiting explicitly says the process varies by role.

Keep unknowns useful: a candidate’s evidence worksheet

The unconfirmed points are actionable recruiter questions, not defects to hide. Exact Tokyo FDE/AI FDE round order, durations and rubric remain unknown. So do assessment rules for AI, search, libraries and IDEs; take-home availability; and live-coding languages. The DSA “Vibe Coding” label and a Manager’s workplace AI-tool requirement do not grant those permissions.

Tokyo engagement duration, concurrent customer count, utilisation targets, quota/bonus, hybrid arrangements and travel calculation are unconfirmed. Production ownership alone establishes neither indefinite 24/7 on-call nor a contractual SLA. Reporting lines and a standard Japan DS/EM structure are also unknown. Ask about each point with the exact requisition, rather than assuming that a service page or URL path supplies it.

Prepare this worksheet offline, using only material you may share. No Databricks workspace, paid API or application submission is needed.

Worksheet field	Evidence to record	Decision it supports
Vacancy identity	Role, Tokyo/other region, requisition, ATS ID, date rechecked	Which requirement set applies
Work boundary	Your responsibility, teammates’ responsibility, customer acceptance	Whether your evidence fits IC, DSA, SA or Manager work
Production record	Architecture, test/release trace, failure, rollback and handoff	What you built and how you verified it
Numbers	Actual baseline, workload, metric definition, before/after, limitations	Whether a claimed improvement is attributable
Trade-off	Alternatives, rejected option, constraint, owner who approved	Why your decision was reasonable
Missing experience	Fact IDs you cannot yet support; an explicitly unexecuted exercise	What to learn without inventing an achievement
Interview contract	Confirmed round names, tools allowed, shareable artifacts	How to prepare within the actual instructions

Start with one real project and three relevant fact IDs. Write what you did, the evidence you can show and the point you still cannot substantiate. A training exercise can fill a learning gap, but label it as an exercise. The technical preparation article and delivery preparation article then provide separate inferred questions grounded in these IDs.