Senior Business Analyst

Experience - 3 to 5 yrs

Key Responsibilities

Technical Domain Skills Required

• Advanced SQL T-SQL : Expert-level database query optimization, view creation, and deep structured analytical querying

• Advanced Programming : Hands-on capability in Python for exploratory data analysis (EDA), hypothesis testing, and implementing fundamental Machine Learning (ML) models

• Microsoft BI Stack : Solid foundation across SSIS, SSAS, and SSRS ecosystems

• Modern Product Analytics : Experience tracking user behavior via Google Analytics, Google Tag Manager, or clickstream events is highly preferred.

  1. Advanced Analytics, Modeling RCA

• Root-Cause Analysis (RCA): Lead complex RCAs on volatile business shifts (e.g., sudden booking drops or cancellation spikes) and deliver quick-turnaround strategic interventions

• Rule Based Frameworks: Apply cohort analysis, funnel tracking, and statistical forecasting techniques to predict peak seasonal travel demand

• Advanced Decision Models: Build heuristic and predictive data models to unlock alternative ancillary revenue streams and operational efficiencies

  2. BI Engineering Data Architecture Management

• Data Visualization Integration: Oversee the construction of enterprise-level Power BI dashboards utilizing complex DAX logic and Power Query to surface executive-level insights

• ETL Pipeline Governance: Supervise the development and continuous execution of data pipelines using SSIS, SSAS, or modern data orchestrators

• Data Quality Assurance: Maintain ownership over data modeling, transformation structures, SQL views, and procedures to guarantee data validity.

  3. Team Leadership Stakeholder Management

• Team Mentorship : Lead, mentor, and define performance benchmarks for a high-performing team of data analysts.

• Cross-Functional Collaboration : Act as the primary consultant for Product Managers, Business Development heads, and Engineering teams to transform raw analytics into real feature deployments

  4. Strategic Business Drive Domain Analytics

• Revenue Pricing Optimization: Analyze demand-supply gaps, inventory utilization patterns, and dynamic pricing effectiveness across routes and geographies

• Conversion Funnel Management: Deep-dive into customer checkout cohorts, shopping cart drop-offs, and cancellation bottlenecks to drive user retention

• Campaign Loyalty Analysis: Partner with marketing teams to design, track, and optimize customer rewards and sales promotion campaigns.

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