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Databricks

Builds cloud software for data analysis and AI

Senior Solutions Architect

Pre-Sales Solution EngineeringConfirmed live in the last 24 hours
Full-timeTokyo, JapanNo salary listedIn-person

About the Job

Requirements

  • At least 5 years of expertise in customer data platforms, marketing technology, or customer data engineering, including at least 3 years in a customer-facing, pre-sales, or consulting role, and a proven record designing and delivering client-facing customer data and activation solutions.
  • Experience designing and implementing data and AI applications for marketing and customer engagement, including identity resolution, segmentation, personalization, next-best-action, and agentic AI workflows for audience building and campaign activation.
  • Deep familiarity with CDP and martech/adtech platforms, including composable CDP architectures such as Salesforce Data Cloud, Adobe, Segment, Treasure Data, Braze, or similar, including implementation, identity resolution, audience activation, and migration strategies.
  • Strong understanding of the customer data landscape, including first-party web, app, CRM, and POS data; third-party and enrichment data; identity graphs; consent and privacy signals; and martech/adtech destinations such as ad platforms, email/SMS, CRM, and analytics tools.
  • Experience operationalizing customer data as governed data products, including defining audiences and segments in SQL or natural language (Genie), reverse-ETL and activation pipelines, and metric, identity, and consent governance in Unity Catalog.
  • Experience collaborating with Global System Integrators and third-party consulting organizations to drive customer outcomes in marketing and customer data initiatives.
  • Hands-on experience building solutions within AWS, Azure, or GCP, with an understanding of cloud-native data ingestion, activation, and governance.
  • An undergraduate degree or higher in a technical field such as Computer Science, Data Science, Applied Mathematics, Engineering, or a similar field.
  • Full professional proficiency in Japanese is mandatory.

Responsibilities

  • Provide technical leadership to guide strategic customers through successful implementations on customer data and marketing projects, ranging from architectural design to data engineering, identity resolution, audience activation, and agent deployment.
  • Collaborate with go-to-market leadership and account teams to design and execute high-impact engagement strategies across the territory, driving Customer Lake adoption from initial Customer 360 build-out through full CDP augmentation or replacement.
  • Serve as a trusted-advisor Solutions Architect, building technical credibility with CMOs, heads of marketing technology, data engineering leaders, and marketing and analytics teams to drive product adoption and vision.
  • Enable clients at scale through workshops, proof-of-concept execution, and customer-facing collateral that increases technical knowledge and demonstrates the value of an embedded, agentic CDP architecture.
  • Translate field-derived, data-driven insights into strategic recommendations for Product and Engineering teams to influence the product roadmap.
  • Act as the tier-3 escalation point for the field, handling complex technical challenges in this product line and ensuring customer success in mission-critical, customer-facing data environments.
  • Establish and refine the sales qualification and proof-of-concept intake process to ensure well-scoped engagements that maximize customer success and minimize friction for research and development.

Desired Qualifications

  • Business-level English proficiency is preferred.

About the Company

Databricks

Builds cloud software for data analysis and AI

Databricks builds cloud software that businesses use to organize data, analyze it, and develop AI applications. Its lakehouse architecture brings the flexible storage of a data lake together with the querying and management tools of a data warehouse. Data engineers prepare and update datasets, analysts build reports, and developers train models or create applications in a shared workspace with controls over who can use the data. The platform runs on Amazon, Microsoft, and Google clouds, and customers pay for computing usage or commit to longer contracts for discounted rates. Databricks was founded in 2013 and is headquartered in San Francisco, California.

Company size
10,001+
Company stage
Late Stage VC
Total funding
$32.1B
Headquarters
San Francisco, CA
Founded
2013

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