Data Architect - GCP

Job opportunity for Data Architect - GCP Based in London, UK - Contract - Onsite

Key skills required for the role:

Data platforms (admin, processing (etl, scripting), migrating, architecture ), Articulate, system/data/processes/framework analysis, build tools/processes using python and LLMs

Should have client facing experience at architecuture, analyst or dba level

Overview

We are seeking an experienced Data Architect to support a Data Strategy Discovery engagement for a global telecommunications client, spanning the Customer, Finance, and Network domains. This role will drive the assessment of the current data landscape — including an existing GCP BigQuery-based Data Lakehouse — and help shape a target-state data architecture, technology roadmap, and governance framework. The ideal candidate combines strong technical data architecture skills with stakeholder-facing consulting ability, and has hands-on familiarity with GCP data platforms.

Key Roles & Responsibilities

Workstream 1 – Scope, Drivers & Stakeholder Alignment

• Participate in stakeholder interviews/workshops to understand current-state architecture, pain points, and business drivers (cost, compliance, reporting gaps, AI/analytics ambitions, scalability).
• Help identify and engage data owners, IT/application owners, SMEs, and technical leads across in-scope domains.
• Contribute to scoping decisions on systems, domains, and deliverable cadence.

Worzstream 2 – Data Landscape Inventory

• Catalogue databases, tech stacks, and data processing methodologies across the in-scope estate, including an existing BigQuery-based Data Lakehouse.
• Map data movement across systems (integrations, ETL/ELT, APIs, manual exports) to surface silos and duplication.
• Group data sources by business domain to identify duplicate/unreconciled data entities.
• Produce a consolidated data source inventory and source-to-target flow diagrams.
• Assess current architecture and platform: warehouse/lake design, integration tooling, cloud vs. on-prem footprint, and scalability constraints.

Workstream 3 – Data Health Assessment

• Assess data quality dimensions (completeness, consistency, duplication, accuracy) via interviews and/or sample profiling.
• Evaluate governance maturity: stewardship, ownership, standards, metadata management, and MDM practices.
• Assess security/compliance posture for PII and sensitive data handling.
• Identify architectural and scalability gaps in the current platform.

Workstream 4 – Synthesis & Roadmap

• Consolidate findings into a prioritized, evidence-based inventory and maturity assessment.
• Design the high-level target-state Data Architecture, including a Data Ontology for AI use cases (Customer 360, churn, order prioritization, assurance/NOC, autonomous network, capacity planning, revenue assurance, etc.).
• Finalize the 7-layered target technology stack for future data strategy phases.
• Define a high-level data governance framework (lineage, pipeline, ownership, lifecycle, visualization).
• Support development of the high-level data strategy roadmap and target operating model, and help drive stakeholder signoff.

Tools & Technologies

Must-Have

• Google Cloud Platform (GCP) — BigQuery (Data Lakehouse architecture, performance, scalability)
• Data architecture & modeling (conceptual, logical, physical; domain-driven data modeling)
• ETL/ELT tools and integration patterns (batch, API-based, CDC)
• Data governance & metadata management concepts (data catalogs, MDM, lineage tools)
• Data quality assessment/profiling methods and tools
• Experience with data architecture in Telecom or similarly complex enterprise environments
• Strong stakeholder engagement, interviewing, and documentation skills (architecture diagrams, inventories, roadmaps)

Good-to-Have

• Experience with GCP ecosystem tools beyond BigQuery: Dataflow, Data Fusion, Dataplex, Vertex AI, Cloud Composer
• Exposure to AI/ML data enablement and data ontology design for AI use cases
• Knowledge of GDPR and telecom-specific regulatory/compliance frameworks
• Experience with legacy on-prem data warehouse/lake platforms and hybrid cloud migration
• Familiarity with data governance/compliance frameworks (DAMA-DMBOK, DCAM)
• Prior consulting/advisory experience delivering data strategy assessments (vs. pure implementation)
• Familiarity with telecom OSS/BSS systems and network data domains

Suggested Experience Range

10–15 years of overall data architecture/engineering experience, with at least 5+ years specifically in data architecture or data strategy consulting/advisory roles, and 2–3 years of hands-on GCP (BigQuery) experience. Telecom domain exposure is a strong plus