Lead Analyst

Key responsibilities

End-to-end problem solving and consulting

• Own client problems from start to finish: frame the business question, design the solution, build it, and land the insight with stakeholders. 2 Job Description – Manager – Marketing Analytics & AI Solutions – For internal use only
• Act as a trusted advisor to client and internal stakeholders; translate marketing and business goals into clear data and analytics requirements.
• Break down ambiguous, cross-functional problems instead of working on siloed tasks; connect data, reporting and AI into one solution.
• Present findings and recommendations in a clear, simple, business-first way.

Marketing analytics and insights

• Lead analysis of campaign, channel and website performance using clickstream and marketing data (e.g., Adobe Analytics, media platforms).
• Build and interpret funnel, journey, attribution and conversion analyses to drive optimisation decisions.
• Define KPIs and measurement frameworks that link marketing activity to business outcomes.

Data engineering and pipelines (Microsoft Fabric)

• Manage ETL/ELT pipelines that ingest marketing and clickstream data from APIs, cloud apps and databases.
• Apply Microsoft Fabric concepts – Lakehouse, OneLake, Data Factory pipelines, Notebooks and the Medallion (Bronze, Silver, Gold) architecture.
• Enforce data quality, governance and documentation; keep pipelines reliable, performant and cost-effective.

Power BI and reporting

• Lead the design and build of

Power BI dashboards and semantic models (data modelling, DAX, row-level security, performance tuning).

• Move reporting from manual effort to automated, self-serve insight for business users.

AI agents and intelligent automation

• Identify use cases where AI agents add value – e.g., insight generation, anomaly detection, reporting commentary, analyst co-pilots.
• Design, build and deploy AI agents, preferably on Azure AI Foundry, integrated with Fabric data and Power BI.
• Judge when rule-based automation (scripts, scheduled pipelines, Power Automate) is the better and cheaper choice than an agent; build the business case on cost, accuracy and effort.
• Monitor agent quality, cost (e.g., token usage) and responsible AI guardrails in production.

Team leadership and operational excellence

• Lead, coach and grow a team of 8–10 analysts and engineers; set clear goals, review work and build skills in AI and Fabric. 3 Job Description – Manager – Marketing Analytics & AI Solutions – For internal use only
• Own operational metrics – utilisation, on-time delivery, SLA adherence, quality/rework, backlog health and client satisfaction.
• Plan capacity, allocate work and manage risks and escalations across multiple workstreams.
• Drive process improvements, reusable assets and automation that raise team productivity.

03 Qualifications and skills

Must-have experience

• 7+ years of experience in marketing analytics, digital analytics or data & analytics roles.
• Proven experience managing ETL/ELT data pipelines end to end.
• Strong hands-on experience building Power BI dashboards and data models.
• Hands-on experience building or deploying AI agents or LLM-based solutions, with a clear view on when automation is the cheaper option.
• Experience leading a team of 8–10 people and managing operational metrics.
• Consulting or client-facing experience, ideally in an agency or professional services setting.

Tools and knowledge (preferred)

 AI agents: Azure AI Foundry (preferred); exposure to Copilot Studio, Semantic Kernel or LangChain/LangGraph is a plus.  Microsoft Fabric: Lakehouse, OneLake, Data Factory pipelines, Notebooks, Medallion architecture.  Clickstream: Adobe Analytics and/or GA4, including raw hit-level data (data feeds / BigQuery export).  Marketing analytics: campaign performance, attribution, funnel and journey analysis.  Power BI: data modelling, DAX, semantic models, performance tuning.  Python and SQL: data manipulation (pandas, PySpark) and advanced SQL.

Good to have

• Microsoft certifications such as DP-600 (Fabric Analytics Engineer), DP-700 (Fabric Data Engineer) or AI-102 (Azure AI Engineer).
• Exposure to Azure Databricks, Alteryx or similar ETL tools.
• Understanding of consent, privacy and data governance in marketing data. 4 Job Description – Manager – Marketing Analytics & AI Solutions – For internal use only

04 Mindset, values and culture

Who you are

• Problem solver: you take ownership of the full problem and see it through.
• Learning mindset: you keep up with fast-moving AI and data tools and share what you learn with the team.
• Hands-on: you are comfortable building, not just reviewing.
• Cost-aware: you weigh value, effort and run cost before picking a solution.
• Clear communicator: you explain complex ideas simply to technical and business audiences.