Req ID: CSQ427R97
Location: Tokyo, Japan
We are FDEs! The Forward Deployed Engineering team is a highly specialized, customer-facing unit within Databricks. We work with strategic customers to design, build, and productionize high-impact data and AI solutions on the Databricks Data Intelligence Platform.
As Manager, Forward Deployed Engineering, you will lead and grow a team of customer-facing engineers focused on solving complex data engineering and AI challenges for some of our most strategic customers. You will help shape delivery strategy, develop senior technical talent, build trusted executive relationships, and ensure our teams deliver measurable customer outcomes at high quality and scale. This is a hands-on leadership role: you will manage and grow the team while remaining the senior technical authority they escalate to.
This role is ideal for a leader who combines strong people management with deep technical credibility in data engineering, platform architecture, and large-scale production delivery.
The impact you will have- Lead, hire, mentor, and grow a team of ~10 high-performing Forward Deployed Engineers, building a strong culture of technical excellence, customer obsession, and execution
- Partner with customers and internal stakeholders to shape complex engagements that deliver meaningful business outcomes on Databricks
- Guide teams through end-to-end solution delivery, from discovery and architecture through implementation, deployment, and enablement
- Build trusted relationships with technical and executive stakeholders, acting as a senior advisor during strategic customer engagements
- Serve as the senior technical escalation point for the team. Stay hands-on enough to dive into architecture reviews, unblock complex delivery issues, and make high-stakes technical calls alongside your engineers on the most strategic accounts.
- Coach engineers on solution design, stakeholder management, delivery quality, and long-term career development
- Partner closely with Sales, Field Engineering, Product, and Professional Services leadership to align on account strategy, staffing, and execution
- Improve repeatability and scale by developing reusable patterns, delivery best practices, technical assets, and team processes
- Help the organization grow its presence in data engineering-led transformations, including modern data platforms, pipelines, governance, and production workloads
- Experience leading, hiring, and developing high-performing technical teams in customer-facing environments
- Current, hands-on technical credibility. Able to personally review designs, debug production issues, and act as the final technical escalation point rather than manage from above.
- Strong background in data engineering, big data systems, and production data platform delivery
- Experience across modern data workloads such as ingestion, transformation, orchestration, warehousing, analytics/BI, or streaming in enterprise environments
- Strong hands-on fluency in Python and SQL; familiarity with distributed processing frameworks (Apache Spark) and JVM languages (Scala/Java) is a plus
- Passion for reinventing delivery through AI. Continuously experiments with and operationalizes the latest AI coding tools (e.g., Genie, Claude Code, Codex) to improve productivity, quality, and repeatability across the team
- Ability to scope and guide complex technical engagements, manage escalations, and ensure successful delivery across multiple stakeholders
- Strong executive communication skills, with the ability to translate technical decisions into business impact, or vice versa
- Comfort operating in ambiguous environments and leading teams through fast-moving, high-visibility customer work
- Experience working with cloud platforms such as AWS, Azure, or Google CloudGCP
- Fluent Japanese is required, and business-level English is required
- Experience delivering lakehouse, analytics, or AI-adjacent solutions in complex enterprise environments
- Deep hands-on understanding of Apache Spark, distributed data processing, and modern data architecture patterns
- Familiarity with data governance, security, and platform operating models
- Experience building reusable delivery frameworks, technical accelerators, or practice-level standards
- Track record of influencing cross-functional strategy across sales, product, and delivery organizations
About Databricks
Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.
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.
Skills Required
- Experience leading, hiring, and developing high-performing technical teams in customer-facing environments
- Hands-on technical credibility; able to review designs, debug production issues, and serve as final technical escalation
- Strong background in data engineering, big data systems, and production data platform delivery
- Experience across modern data workloads (ingestion, transformation, orchestration, warehousing, analytics/BI, streaming)
- Strong hands-on fluency in Python and SQL
- Familiarity with distributed processing frameworks (Apache Spark) and JVM languages (Scala/Java)
- Experience working with cloud platforms such as AWS, Azure, or Google Cloud (GCP)
- Fluent Japanese
- Business-level English
- Ability to scope and guide complex technical engagements and manage escalations across stakeholders
- Strong executive communication skills and experience building trusted relationships with technical and executive stakeholders
- Experience delivering lakehouse, analytics, or AI-adjacent solutions in complex enterprise environments
- Experience building reusable delivery frameworks, technical accelerators, or practice-level standards
- Passion for operationalizing AI and using AI coding tools to improve delivery (e.g., Genie, Claude Code, Codex)
Databricks Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Databricks and has not been reviewed or approved by Databricks.
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Equity Value & Accessibility — Equity grants are a meaningful part of offers, and periodic tender opportunities and secondary options have made private equity more tangible for many employees. This perceived upside contributes to strong total-compensation sentiment in key roles.
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Healthcare Strength — Comprehensive medical, dental, and vision coverage is paired with mental‑health resources and wellness reimbursements, indicating a robust health package. Multiple summaries highlight broad coverage that employees can practically use.
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Leave & Time Off Breadth — Generous PTO, paid holidays/sick time, and fully paid parental leave are frequently described, with hybrid/remote flexibility common in the U.S. These policies expand time‑off accessibility across different life stages.
Databricks Insights
What We Do
As the leader in Unified Data Analytics, Databricks helps organizations make all their data ready for analytics, empower data science and data-driven decisions across the organization, and rapidly adopt machine learning to outpace the competition. By providing data teams with the ability to process massive amounts of data in the Cloud and power AI with that data, Databricks helps organizations innovate faster and tackle challenges like treating chronic disease through faster drug discovery, improving energy efficiency, and protecting financial markets.
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