Analytics Manager - R01563909

Primary Skills

• Engagement Management
• Solutioning/Pre-Sales
• Client management
• Multiple project handling
• SOWs
• Domain expertise
• Should have technical background in BI/Data Engineering/Data Science
• SQL Expertise
• Graduate from Tier1 and Tier2 institutes only (BE/B Tech)

Specialization

• Certification in Cloud/BI/ML

Job requirements

Job Description

Analytics Solution / Engagement Manager 

Experience: 8–15 Years
Location: India (Client-facing / Hybrid)
Role Type: Client Engagement | Analytics Solutions | Leadership

Role Overview

We are seeking a seasoned Analytics Solution / Engagement Manager to lead end‑to‑end analytics engagements for strategic clients. This role requires a strong blend of hands-on analytics expertise, solutioning capability, client relationship management, and AI-driven thinking, with a proven ability to deliver measurable business value through advanced analytics.

The incumbent will act as a trusted analytics advisor to clients, owning solution design, delivery governance, stakeholder management, and expansion opportunities across complex, multi‑practice engagements.

Key Responsibilities

1. Client Engagement & Expectation Management

• Serve as the primary analytics engagement lead for key client accounts, managing senior stakeholders (CXO, VP, Director level).
• Own client expectation management, ensuring clear alignment on business goals, scope, timelines, and outcomes.
• Drive analytics storytelling, translating complex data insights into clear, actionable narratives for business decision‑makers.
• Lead and present MBRs/QBRs, executive dashboards, and impact assessments, showcasing ROI and business outcomes.
• Proactively identify risks, dependencies, and roadblocks, and resolve them through independent research and structured problem-solving.

2. Analytics Solutioning & Delivery Leadership

• Lead problem framing and requirement gathering from a business and analytics perspective, converting ambiguous asks into structured analytics use cases.
• Define solution approach, architecture, and analytics roadmap in collaboration with data engineering, BI, and data science teams.
• Provide hands‑on guidance across:
• Analytics & Insights: Advanced SQL, Python/PySpark, exploratory analysis
• Business Intelligence: Power BI / Tableau (data models, dashboards, performance optimization)
• Data Engineering: ETL pipelines, data modeling, cloud data platforms
• Advanced Analytics / Data Science: Predictive or prescriptive modeling (preferred)
• Ensure analytical rigor, data quality, and scalability of solutions delivered.

3. Proposal Management & Pre-Sales Support

• Lead or contribute to client proposals, including:
• Solution design and effort estimation
• Analytics approach and delivery model
• Governance, staffing plans, and value articulation
• Actively support and respond to RFIs/RFPs, showcasing domain knowledge, solution strength, and differentiation.
• Collaborate with sales and leadership teams to drive account growth and deal renewals.

4. Cross‑Practice & Team Collaboration

• Work in a cross‑practice collaboration model, aligning analytics with data engineering, AI/ML, visualization, and domain consulting teams.
• Provide mentorship and technical leadership to analysts, data engineers, and senior consultants.
• Establish delivery best practices, governance frameworks, and execution standards across engagements.

5. AI‑Driven & Forward‑Looking Analytics Mindset

• Bring an AI‑driven mindset to analytics engagements, identifying opportunities to leverage:
• AI/ML models
• Generative AI and automation
• Advanced optimization and forecasting techniques
• Continuously explore emerging analytics, AI, and data technologies to enhance client value and internal capabilities.

Domain Expertise (Mandatory)

Strong analytics and business knowledge in one or more of the following domains is mandatory:

• IIT/NIIT background
• Utilities
• Construction / Engineering
• Retail / Consumer Analytics
• Textile & Packaging
• Supply Chain & Operations Analytics

Ability to contextualize analytics solutions within domain‑specific KPIs, workflows, and decision frameworks is critical.