Head of Data Engineering & Analytics (Grade 5)

by

SFIA-8 Aligned

Overview

As Head of Data Engineering & Analytics (Grade 5), you provide strategic oversight and operational leadership for the design, delivery, and assurance of data pipelines, analytics platforms, and trusted reporting across the business. This role brings together Data Governance, Engineering, Reporting, and Advanced Analytics into a unified, high-confidence service that enables accurate decision-making.

You are accountable for ensuring business-critical metrics — such as workforce headcount, financial forecasts, project performance, and business development pipeline — are reliable, reconcilable, and timely. You influence enterprise-wide data maturity through standards, automation, and enablement, while championing responsible innovation, including the use of AI and modern analytics tools.

This role supports the sustainable delivery of data services across People, Projects, Finance, and Strategic domains. You balance business-as-usual operational integrity with forward-thinking capability uplift, acting as a trusted advisor to senior leaders and an enabler of consistent insight.

Key Responsibilities

1. Trusted Reporting & Assurance Across Domains

(SFIA: Data Analysis – DTAN Level 5, Data Management – DATM Level 5)

  • Provide traceable, reliable, and validated reporting for key domains including HR, Projects, Finance, and Pipeline.
  • Own data reconciliation frameworks (e.g. workforce = opening + joiners − leavers).
  • Define data validation controls and lineage documentation to assure output.

2. Strategic Oversight of BAU Data Operations

(SFIA: Systems Development Management – DLMG Level 5, Application Support – ASUP Level 5)

  • Oversee the daily health, scheduling, and delivery of core data pipelines and reports.
  • Maintain a robust metadata-driven environment to support consistency and change management.
  • Define SLAs, manage incidents, and support version-controlled continuous improvement.

3. Cross-Functional Leadership (Governance, Engineering, Reporting, Advanced Analytics)

(SFIA: Stakeholder Relationship Management – RLMT Level 5, Programme Management – PGMG Level 5)

  • Lead unified delivery across the four pillars of data maturity: Governance, Engineering, Reporting, and AI/Analytics.
  • Ensure these functions are aligned to business outcomes, with shared definitions, priorities, and responsibilities.
  • Provide assurance and support for enterprise initiatives requiring high-trust data and insight.

4. Responsible AI & Modern Analytics Enablement

(SFIA: Artificial Intelligence – AIRE Level 5, Analytics – INAN Level 5)

  • Lead implementation of AI-driven solutions such as anomaly detection, forecasting, and record classification.
  • Champion explainable, validated AI aligned to governance and ethical best practice.
  • Collaborate with analysts and data scientists to introduce repeatable AI pipelines where appropriate.

5. Metadata, Data Standards & Lineage

(SFIA: Metadata Management – METL Level 5, Information Governance – GOVN Level 5)

  • Define and embed metadata models and business glossaries to support trust and traceability.
  • Establish and enforce standards for data definitions, lineage, and catalogue integration.
  • Support the development of a system-wide data dictionary for cross-domain clarity.

6. Enablement & Culture of Quality

(SFIA: Leadership – LEAD Level 5, Quality Assurance – QUAS Level 5)

  • Mentor engineers and analysts to embed data quality checks and reusable components.
  • Publish operational guidance and internal standards through structured knowledge sharing.
  • Contribute to the Data & Analytics Operating Model and team performance strategy.

What You Bring…

✅ Strategic Data Leadership

  • Experience leading cross-functional teams across engineering, reporting, governance, and analytics.
  • Demonstrated ability to influence and align senior stakeholders across business units.
  • Clear understanding of how data enables confident decisions and operational success.

✅ Trusted Reporting & Assurance Thinking

  • Expertise in building reconciled, validated reports that explain and withstand scrutiny.
  • Strong understanding of headcount, financial, and project metrics across the data lifecycle.
  • Ability to explain pipeline logic, identify mismatches, and trace root causes.

✅ Engineering Fluency & Metadata Design

  • Proficient in SQL, data pipeline orchestration, and metadata-driven engineering approaches.
  • Experience implementing data catalogue, lineage, and quality control tools.
  • Familiarity with Lakehouse architecture, semantic models, and cloud-native data services.

✅ Modern Analytics & AI Governance

  • Knowledge of deploying AI tools for insight and anomaly detection.
  • Understanding of explainability, validation, and responsible use of machine learning.
  • Experience introducing new technologies into a BAU environment with confidence and control.

✅ Enablement & Delivery Culture

  • Passionate about improving team maturity through reusability, clarity, and autonomy.
  • Effective communicator, able to guide others with clarity and technical integrity.
  • Collaborative mindset with strong internal publishing, mentoring, and uplift behaviours.

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