Data AS Consultant, APJ

Vollzeit
vor 1 Tag

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
  • 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.
  • 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.
  • 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.
  • 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.

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