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 – 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 – 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 – 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 rapid-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.

📌 Lead Analyst (Bengaluru)
🏢 Dentsu Global Services
📍 Bengaluru