Solution Architect - Data & Analytics

Location

Role Overview

Discovery & Gap Assessment

• Lead stakeholder workshops with regional analytics, marketing, and e-commerce leads to map current data landscape.
• Classify data gaps precisely — connectivity (no pipe exists), governance (mapping/master data unresolved), or access (data exists but sits with a third party) — since each has a different fix and cost.
• Assess existing central platform capabilities (e.g. Databricks Lakeflow Connect, AI Search/RAG, Delta Lake, Clean Rooms) to determine what can be extended vs. what needs new tooling.

Architecture & Solution Design

• Design end-to-end data unification architecture spanning structured (ERP/CRM) and previously-missing sources (performance marketing, social, e-commerce).
• Favor native platform capabilities over third-party tooling where a supported native path exists, to avoid unnecessary tool sprawl and stay aligned with the client's own platform strategy.
• Produce architecture blueprints, technical documentation, and a phased implementation roadmap.

Business Case & Roadmap

• Translate technical gaps into a business case with clear ROI framing — tied to concrete use cases (e.g. campaign performance visibility, regional reporting parity with global).
• Define success metrics and phased delivery plan (pilot domain first, then rollout).

Client Advisory

• Act as the primary technical advisor to the client's regional analytics/marketing leadership throughout discovery.
• Where appropriate, help the client's internal sponsor build the case to secure budget or sign-off from central/global stakeholders.

Presales & Handoff

• Convert discovery findings into a scoped, fixed-fee proposal for the next phase (pilot or full implementation).
• Bring in specialist skillsets (marketing/digital analytics, AI automation/extraction) only where discovery confirms the need — avoid over-scoping upfront.

Required Skills & Experience

• 10–15 years of experience in Solution Architecture, Data Engineering, or Analytics, with genuine depth in at least one modern lakehouse/cloud data platform (Databricks strongly preferred; Microsoft Fabric, Snowflake, Azure Data Services acceptable).
• Direct experience with Databricks-native ingestion and unification capabilities (Lakeflow Connect, Delta Lake, AI Search/RAG, Clean Rooms) — able to speak to these specifically, not just generically about "the lakehouse."
• FMCG/CPG or retail domain experience — comfortable with SKU/product master data, trade promotion structures, and regional-vs-global data ownership models.
• Proven discovery/advisory experience — workshop facilitation, as-is/to-be mapping, business case and ROI development.
• Working understanding of the marketing/e-commerce data landscape (ad platform APIs, marketplace seller APIs, social listening tools) — deep enough to scope accurately, even if a specialist handles hands-on build.
• Strong executive communication — able to engage credibly with both technical stakeholders (data science background) and commercial sponsors.

Preferred Qualifications

• Databricks certification (Solutions Architect or equivalent).
• Experience with clients running centralized global data platforms with regional onboarding gaps (multinational FMCG, retail, or consumer goods).
• TOGAF or equivalent enterprise architecture certification.
• Prior experience shaping the business case for data/analytics engagements that convert from paid discovery into full implementation.

Success Measures

• Accurate gap classification (connectivity vs. governance vs. access) validated by the client.
• Roadmap and business case that converts into a signed implementation engagement.
• Architecture recommendations that align with, rather than duplicate, the client's existing platform investment.
• Client trust — measured by willingness to expand scope (additional markets, additional data domains) after Phase 1.