Senior Manager - Data & Analytics

You are as unique as your background, experience and point of view. Here, you'll be encouraged, empowered and challenged to be your best self. You'll work with dynamic colleagues - experts in their fields - who are eager to share their knowledge with you. Your leaders will inspire and help you reach your potential and soar to new heights. Every day, you'll have new and exciting opportunities to make life brighter for our Clients - who are at the heart of everything we do. Discover how you can make a difference in the lives of individuals, families and communities around the world.

Job Description:

Title: Sr. Manager, Data Analytics (Business Reporting and Intelligence)

Job Band: 6.2

Direct Reports: 10-15

Shift: 1:30 pm - 10:00 PM IST

Experience: 10-14 Years

Location: Gurgaon

Job Overview

We are seeking an experienced and hands-on analytics leader to head the Business Reporting & Intelligence function and drive the organization's transition to a scalable, standardized, and insight-driven analytics ecosystem.

This role is a player-coach position, combining individual technical contribution with people leadership. A critical expectation of the role is the ability to identify high-value analytics and machine-learning use cases, determine which analytical or ML techniques are appropriate for specific business problems, and ensure solutions are pragmatic, interpretable, and scalable.

The role spans different functions and insurance business groups, and operates within a Global Capability Center (GCC) environment, requiring close collaboration with onshore stakeholders and data engineering teams to deliver actionable insights and measurable business impact.

Key Responsibilities

1. Reporting & Analytics Modernization

• Lead the transition from manual, Excel-driven reporting to automated, standardized analytics solutions.
• Design and govern single-source-of-truth datasets and data marts across key business domains.
• Define and institutionalize KPI frameworks, metric definitions, and reconciliation standards.
• Oversee migration and optimization of legacy reporting into a Tableau-centric and GenBI-enabled analytics environment, supporting both self-service and AI-assisted insight consumption.
• Partner with business and engineering teams to balance central governance with decentralized data ownership, ensuring scalability, consistency, and speed.

2. Advanced Analytics & Machine Learning (Hands-On)

• Act as a hands-on individual contributor on complex analytics initiatives while also leading the team.
• Identify, evaluate, and prioritize analytics and ML use cases based on business value, data readiness, and interpretability.
• Apply appropriate statistical and machine-learning techniques (e.g., regression, classification, segmentation, forecasting) to business problems
• Demonstrate strong judgement on when ML is required versus when simpler analytical approaches are more effective.

3. AI-Assisted & Intelligent Automation

• Identify high-value business use cases where AI-assisted analytics can improve insight generation, analytical efficiency, or decision support.
• Lead proof-of-concept (POC) initiatives by translating business problems into clearly scoped analytical or AI-assisted solutions.
• Partner with business stakeholders to pitch, validate, and refine AI-enabled ideas, ensuring alignment to real operational needs and data readiness.
• Apply strong judgement to determine when AI-assisted approaches are appropriate versus traditional analytics, keeping solutions pragmatic and interpretable.
• Support leadership decision-making by demonstrating the potential impact, limitations, and adoption considerations of AI-assisted analytics initiatives.

4. Data Architecture, Cloud & Engineering Collaboration

Partner closely with Data Engineering and Technology teams to:

• Define scalable data ingestion and transformation patterns
• Build automated pipelines using SQL, Python, and ETL frameworks
• Leverage cloud-based analytics platforms such as Snowflake and AWS
• Ensure high standards of data quality, governance, security, and access control.

5. Stakeholder Partnership & GCC Operating Model

• Operate effectively within a Global Capability Center (GCC) model, partnering with business stakeholders across Sun Life geographies.
• Translate ambiguous and evolving business requirements into structured analytics roadmaps.
• Manage expectations in a matrixed, multi-time-zone environment, influencing outcomes without direct authority.
• Present insights with strong business context, clear narratives, and decision implications.

6. People Leadership & Capability Building

Lead, mentor, and develop a high-performing analytics team (Analysts, Senior Analysts, Data Scientists).

Drive capability building across:

• SQL and Python
• Tableau best practices and performance optimization
• Statistics, ML foundations, and analytical reasoning
• Analytics automation and efficiency techniques

Establish reusable analytics frameworks, templates, and internal playbooks.

Core Skills & Experience (Must-Have)

Analytics & Technical

• 10-14 years of progressive experience in analytics, reporting, or data science roles.
• Strong hands-on expertise in SQL and Python.
• Proven experience applying statistical and machine-learning techniques in real business contexts.
• Deep experience with Tableau for analytics and storytelling.

Cloud & Platforms

• Experience working with Snowflake and AWS-based analytics ecosystems.
• Ability to collaborate on scalable, production-grade data pipelines and analytics solutions.
• Experience handling large, multi-source operational datasets.

Leadership & Business

• Proven experience managing analytics teams in a GCC or enterprise environment.
• Strong business judgement and ability to operate in ambiguity.
• Excellent communication skills to explain analytical outcomes to non-technical stakeholders.

Preferred / Future-Ready Capabilities

• Experience with AI-assisted analytics, intelligent automation, or decision-support systems.
• Exposure to GenAI or agentic AI in analytics contexts (use-case driven).
• Experience building scalable analytics assets or data products, not just dashboards.
• BFSI / Insurance / Operations analytics exposure is a strong advantage.

Leadership Profile

• Strong analytical judgement - knows where ML adds value and where it does not.
• Hands-on leader who leads by example.
• Builder mindset - focuses on sustainable systems over one-off outputs.
• Business translator - bridges analytics, technology, and decision-making.
• GCC-aware leader - effective in matrixed, multi-geo operating models.

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