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Data Architect
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Role: Data Architect
Location: Reading and all Colt location
Mode: All 5 days onsite
JD
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 architecture, 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.
Workstream 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.
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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.
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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.
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