Senior Manager, Data Architecture & Analytics

About the role

The Senior Manager, Data Architecture & Analytics leads client engagements that transform fragmented data environments into coherent, trusted architectures. You will bridge business priorities, governance, and technology to deliver scalable designs, roadmaps, and analytics solutions that enable secure, data‑driven decision‑making.

Key responsibilities

• Lead architecture, modelling, integration, engineering, platform and analytics workstreams across multiple client projects.
• Assess current data landscapes, information flows, platforms, interfaces and analytical capabilities.
• Develop architecture principles, current‑state and target‑state models, transition states and implementation roadmaps.
• Guide conceptual, logical and physical data modelling aligned with business definitions and metadata.
• Define integration and interoperability patterns, including APIs, pipelines, events and controlled data sharing.
• Shape analytics, indicator, dashboard and data‑product architectures linked to business outcomes.
• Incorporate security, privacy, classification, quality, lineage, retention and resilience requirements into designs.
• Facilitate architecture reviews, prepare options and recommendations for client approval.
• Direct technical teams, enforce design quality gates and resolve cross‑workstream dependencies.
• Review technical specifications, models, interface designs and transition plans before client submission.

Required profile

• Bachelor’s degree in Computer Science, Data Engineering, Information Systems or related field; Master’s preferred.
• 10+ years of experience in data architecture, data engineering or analytics.
• At least 4 years of technical leadership experience managing data‑architecture, engineering, integration or analytics programmes.
• Proven ability to lead multidisciplinary teams and deliver complex data solutions.

Required skills

• Data architecture design
• Data modelling (conceptual, logical, physical)
• Data integration and API design
• Data engineering and pipeline development
• Analytics and dashboard creation
• Data security and privacy implementation
• Data governance, quality and lineage management
• Platform evaluation and tool selection