Senior Data Engineer
Role details
Job location
Tech stack
Job description
Mobilization & Current-State Assessment
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Establish engagement plan, governance cadence, interview schedule, and workstream structure
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Confirm in-scope verticals, stakeholder groups, and required artefacts
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Conduct stakeholder interviews and working sessions across pillars and functions
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Document current-state operating model, processes, decision points, and friction points
Domain Coverage
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Cryptography & Secrets: key/certificate lifecycle, secrets onboarding and rotation, vault operations, privileged secrets handling, service-request fulfilment
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Data Leakage Prevention: DLP policy/ruleset engineering, alert triage and tuning, governance workflows
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Data Asset Protection: data scanning operations, permissions/entitlement analytics, DSAR workflow support, remediation coordination
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Policy & Architecture: standards authoring, architecture review support, control interpretation
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Fleet Enablement, Development & Engineering: roadmap governance, SDLC standardization, engineering standards, tooling consistency
AI Opportunity Identification & Cataloguing
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Identify AI-enabled use cases across processes, workflows, and engineering delivery for each in-scope pillar
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Define opportunities for pillar-specific agents and a cross-pillar orchestration agent
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Populate and maintain the AI Initiatives Tracker (Innovation Backlog, AI Initiatives Register, Risk & Controls, Benefits Tracking)
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Map current or recommended AI tooling (e.g., M365 Copilot, GitHub Copilot, Claude Code, ChatGPT) to each use case
Feasibility, Controls & Roadmap
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Assess technical feasibility, control requirements, and data-handling implications for prioritized use cases
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Define target-state operating model, human-in-the-loop points, and governance forums
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Prioritize opportunities and build a phased implementation roadmap (near/medium/long-term)
Executive Reporting & Knowledge Transfer
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Prepare executive-ready findings report and leadership presentation
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Lead validation workshops and readout/knowledge-transfer sessions with client stakeholders
Requirements
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Senior-level consulting experience in data security, data protection, or cybersecurity advisory engagements, ideally within Tier-1 financial services
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Working knowledge across two or more of: cryptography/secrets management (e.g., HashiCorp Vault, PKI), DLP platforms, data discovery/classification and scanning tools, or security architecture and policy
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Demonstrated experience identifying and scoping AI/automation opportunities within enterprise operating models
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Strong stakeholder management skills; comfortable leading interviews, workshops, and executive readouts
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Experience producing operating-model documentation, use-case catalogues, and roadmap artifacts
Preferred Qualifications
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Prior experience with AI initiative tracking/governance frameworks (risk & controls, benefits tracking)
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Familiarity with enterprise AI tooling landscape (Copilot Studio, GitHub Copilot, Claude Code, agent orchestration)
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Background in large-scale financial services technology or security transformation programs