Associate Partner - Customer Analytics

About Rategain
RateGain Travel Technologies Limited is a global provider of AI-powered SaaS solutions for travel and hospitality, working with 13,000+ customers and 700+ partners across 160+ countries. RateGain helps travel and hospitality businesses accelerate revenue generation through acquisition, retention, and wallet share expansion.
Today, RateGain is one of the world's largest processors of electronic transactions, price points, and travel intent data, enabling revenue management, distribution, and marketing teams across hotels, airlines, metasearch companies, package providers, car rentals, travel management companies, cruises, and ferries to drive better business outcomes.
Founded in 2004 and headquartered in India, RateGain works with 33 of the Top 40 Hotel Chains, 4 of the Top 5 Airlines, 7 of the Top 10 Car Rental companies, and all leading Destination Marketing Organizations, Online Travel Agents, and metasearch platforms, including 25 Global Fortune 500 companies, unlocking new revenue every day.
Recognized globally for its innovation and impact, RateGain has been named Best Digital Marketing Agency by HotelTechReport Awards and Best Channel Manager by the World Travel Tech Awards. The company has also been certified as a Great Place to Work® in the USA, Spain, and India, with recognition in India for seven consecutive years. In 2025, RateGain was honored as Emerging Company of the Year at The Economic Times Awards for Corporate Excellence, named among Deloitte India Technology Fast 50 for the sixth time, and won Excellence in Best AI Solution Showcase for UNO VIVA at the CII National AI Awards 2025.
The Mission
Turn data into decisions. The Consultant – Analytics & AI will lead the design and delivery of high-impact dashboards, data-driven case studies, and executive insights that help leadership, Marketing, and PR tell compelling, evidence-backed stories.
The role combines BI and data storytelling, data engineering on Google Cloud (BigQuery), and applied AI – including building AI agents on Google Cloud and using AI-assisted development tools such as Claude Code and Cursor. The individual will lead a small team of analysts, work independently with high ownership, and partner closely with stakeholders across the business.
Key Responsibilities
Data Storytelling & Leadership Insights
Build compelling, insight-led narratives and case studies for leadership and executive audiences, translating analysis into clear business impact.
Design and deliver impactful, executive-ready dashboards aligned to business KPIs and strategic priorities.
Support Marketing and PR with data-backed stories, industry trend reports, and proof points for internal and external communication.
Present findings to senior stakeholders with clarity and confidence, focusing on the “so what” and recommended actions.
BI & Dashboard Delivery
Own end-to-end dashboard delivery – requirement gathering, data modelling, build, QA, deployment, adoption, and iteration.
Develop dashboards across Tableau, Power BI, Looker, and Looker Studio, selecting the right tool for each audience and use case.
Conduct deep-dive analysis on large, complex datasets to uncover trends, patterns, and actionable recommendations.
Define and maintain consistent metric definitions and ensure data quality, integrity, and accuracy across all deliverables.
Data Engineering (Google Cloud)
Write advanced, optimised SQL in BigQuery (partitioning, clustering, window functions, cost and performance tuning).
Build and maintain data pipelines and transformation layers using Python and GCP services such as BigQuery, Cloud Storage, Dataflow, Cloud Composer/Airflow, Cloud Functions, and Cloud Run.
Contribute to data modelling, schema design, and automation of recurring reporting workflows.
Applied AI & Agents
Design, build, and deploy AI agents using Google Cloud services – e.g., Vertex AI, Gemini models, Vertex AI Agent Builder / Agent Development Kit (ADK), Cloud Run, and Cloud Functions – to automate analysis, reporting, and insight generation.
Use AI-assisted development tools (Claude Code, Cursor) to accelerate SQL, Python, pipeline, and dashboard development, and help the team adopt these practices responsibly.
Identify opportunities to apply GenAI (e.g., natural-language querying, automated insight summaries) across team workflows.
Team Leadership & Stakeholder Management
Lead, mentor, and review the work of analysts; plan and prioritise team workload and set delivery and quality standards.
Act as the primary point of contact for stakeholders – scope requests, manage expectations, communicate progress, and present outcomes.
Work independently with high ownership, taking ambiguous problems from question to recommendation.
Functional KPI's
Timely, accurate delivery of dashboards, reports, and analyses against agreed timelines.
Adoption and active usage of dashboards by leadership and business stakeholders.
Data accuracy and consistency of metrics across all deliverables.
Number and quality of leadership-ready case studies and data stories delivered.
Turnaround time for stakeholder requests and ad-hoc analysis.
Reliability and performance of data pipelines and BigQuery workloads (including cost efficiency).
Stakeholder satisfaction with the quality and clarity of insights delivered.
Strategic KPI's
Measurable reduction in manual reporting effort through automation and AI agents.
Successful deployment of AI agents / GenAI use cases that improve speed or quality of insights.
Increased use of data and insights in leadership decisions, Marketing campaigns, and PR stories.
Standardisation of BI practices, metric definitions, and documentation across tools.
Growth and capability-building of the analyst team through mentoring and best practices.
Adoption of AI-assisted development practices (Claude Code, Cursor) across the team.
Functional Competencies
SQL & BigQuery: Advanced SQL with strong hands-on experience in Google BigQuery.
Python: Strong Python for data wrangling, analysis, automation, and pipeline development (pandas, APIs, scheduling).
Data Engineering: Experience building or maintaining ETL/ELT pipelines and data models on a cloud platform (GCP preferred); dbt or Dataform exposure is a plus.
AI Tooling: Certification in, or demonstrable hands-on experience with, Claude Code and/or Cursor for AI-assisted development.
AI Agents on GCP: Hands-on experience creating AI agents or LLM-powered workflows using Google Cloud services (Vertex AI, Gemini, Agent Builder/ADK, or similar).
Data Storytelling: Proven ability to convert complex analysis into clear narratives, dashboards, and case studies for leadership.
Domain: AdTech / MarTech experience preferred – programmatic advertising, campaign measurement, attribution, audiences, and metrics such as impressions, CTR, CPA, and ROAS.
BI Tools – Proficiency Expectations
Most of our current work is in Tableau; however, we are looking for very strong, well-rounded experience across BI tools, with Power BI as the key strength.
Tool
Expected Level
Priority
What “good” Looks Like
Power BI
Expert
Must-have
DAX & Power Query (M), star-schema data modelling, RLS, performance tuning, Power BI Service (workspaces, gateways, refresh, deployment pipelines).
Tableau
Advanced
Must-have
LOD expressions, table calculations, parameters & actions, extracts vs live connections, Tableau Server/Cloud publishing and governance.
Looker (LookML)
Intermediate – Advanced
Preferred
Building and maintaining LookML views, explores, and derived tables/PDTs; governed semantic layer on BigQuery.
Looker Studio
Intermediate – Advanced
Preferred
Blended data sources, calculated fields, BigQuery connectors, parameter controls; fast, shareable stakeholder reports.
Behavioural Competencies
Storyteller mindset – enjoys finding the “so what” in data and presenting it in a way that moves leaders to act.
Strong sense of ownership; able to work independently with minimal supervision.
Proven team leadership – coaching, reviewing, and raising the quality bar for the team.
Excellent written, verbal, and presentation skills for technical and non-technical audiences.
Strong stakeholder management – builds trust, manages expectations, and communicates proactively.
Curiosity about AI and emerging tools, with a practical, results-focused approach.
Strong attention to detail, data accuracy, and quality.
Comfortable with ambiguity and shifting priorities in a fast-paced, high-growth environment.
Education & Experience
Bachelor

Work arrangement
Hybrid