Data AS Consultant, APJ

This role requires a strong blend of technical expertise, customer-facing acumen, and solution-oriented problem solving. The Data AS Consultant will work closely with Sales & Account Managers to provide technical leadership and solution design for highly consultative enterprise projects. It is a revenue-generating pre-sales position focused on enabling technical adoption and driving customer success for both new and expansion opportunities.

This role reports directly to the Team Manager, DSI, with a dotted line to the Regional Head of Japan.

Responsibilities

Technical Consultation & Solution Design

• Serve as a trusted technical advisor to enterprise customers, translating business goals into scalable, production-grade technical architectures.
  
  
• Design and document data integration architectures leveraging Similarweb’s APIs, DaaS feeds, and platform products to meet enterprise data requirements.
  
  
• Architect end-to-end data delivery pipelines into client ecosystems (e.g., Snowflake, Databricks, S3, or Google Cloud Platform).
  
  
• Provide technical workshops and POCs, guiding clients on ingestion, normalization, and querying best practices.
  
  
• Develop Statements of Work (SOWs), technical proposals, and detailed integration documentation to accelerate collaboration between internal teams and customers.
  
  

Technical Skill Execution Examples

1. Integration & Data Delivery
• Role: Design and implement integration workflows delivering Similarweb’s data via API, S3, Snowflake, or GCP Storage, tailored to customer infrastructure.
  
  
• Technical Skills:
• Advanced SQL querying and performance tuning in Databricks / Snowflake
• Proficiency with data file formats (CSV, JSON, Parquet, Delta) and understanding the trade-offs between performance, compression, and schema evolution
• Strong knowledge of ETL design, data governance, and access control
• Outcome: Clients understand technical feasibility and best practices for incorporating Similarweb data into their analytical stack.
  
  
2. Data Quality, Coverage, and Scalability
• Role: Explain how data is collected, validated, deduplicated, and scaled across geographies and verticals, referencing internal reruns and benchmarks.
  
  
• Technical Skills: Understanding of data validation, sampling frameworks, distributed storage performance, and data versioning (e.g., Delta Lake advantages).
  
  
• Outcome: Clients gain trust in data quality and scalability across enterprise-grade analytics environments.
  
  
3. Data Sampling & Proof-of-Concept Delivery
• Role: Generate and deliver custom data samples aligned with client use cases, whether via API queries, Parquet exports, Delta tables, or CSV snapshots.
  
  
• Technical Skills:
• Hands-on data extraction and transformation using SQL and Python
• Preparing data in optimal formats (e.g., Parquet v Delta)
• Managing delivery through multiple mechanisms: secure S3 share, Snowflake Data Share, API endpoints, or CSV download portals.
  
  
• Outcome: Clients can validate data structure, schema, and use-case fit before full-scale adoption.
  
  
4. Use Case Customization & Advanced Analytics
• Role: Demonstrate how Similarweb data can be enriched with client datasets (POS, CRM, or loyalty).
  
  
• Technical Skills: SQL and Python for data joins, REST and GraphQL APIs, JSON schema design, and familiarity with Model Context Protocol (MCP) for LLM integrations.
  
  
• Outcome: The client sees practical, technically sound pathways to integrating Similarweb data into their own analytical models and AI workflows.
  
  

Collaboration with Sales & Customer Teams

• Partner with Enterprise Sales Managers to qualify and design technical solutions during the sales process.
  
  
• Work with Enterprise Account Managers to drive expansion and deepen platform adoption through technical enablement.
  
  
• Collaborate closely with Customer Success and Delivery teams to ensure smooth technical transitions from pre-sales to production.
  
  
• Create reusable demos, templates, and data delivery playbooks for use across GCR teams.
  
  
• Participate in strategic account planning and contribute feedback to product and data engineering based on customer needs.
  
  

Market & Product Expertise

• Stay up to date with AI/LLM data standards, data engineering best practices, and cloud-native architectures.
  
  
• Provide feedback to product and data engineering on API performance, schema design, and data format optimization.
  
  
• Maintain familiarity with Model Context Protocol (MCP), OpenAPI, and emerging standards for data interoperability and contextual AI integrations.
  
  

Project Support & Technical Governance

• Lead technical scoping and architecture reviews during the pre-sales phase.
  
  
• Oversee sample generation, performance validation, and schema testing.
  
  
• Provide escalation support on data performance, pipeline reliability, and delivery troubleshooting.
  
  
• Ensure solutions align with data security, privacy, and compliance best practices.
  
  

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or related field.
  
  
• 5+ years in pre-sales, solution architecture, or data consulting, with a strong focus on technical delivery.
• Technical Expertise:
• Data Architecture & Integration: Deep understanding of cloud ecosystems (AWS, Azure, GCP) and data warehousing frameworks (Snowflake, Databricks, Redshift).
  
  
• SQL & Data Modeling: Strong query and optimization skills for data analysis and transformation.
  
  
• APIs & Data Exchange: REST and GraphQL API fluency, JSON schema design, authentication (OAuth 2.0, JWT).
  
  
• File Formats: Deep understanding of CSV, JSON, Parquet, and Delta file formats—knowing when to use each for efficiency, scalability, or incremental updates.
  
  
• Data Sample Delivery: Ability to create, test, and deliver sample datasets across APIs, S3, and warehouse sharing mechanisms.
  
  
• MCP & AI Integrations: Awareness of Model Context Protocol, LLM context injection, and structured data retrieval patterns.
  
  
• Proven experience translating business requirements into technical solutions for large enterprise data ecosystems.
  
  
• Excellent communication and presentation skills—able to bridge the gap between technical and commercial teams.
  
  
• Fluent in Japanese and English (written and spoken) with experience serving enterprise clients.
• Strong problem-solving, self-management, and team collaboration skills.