DGM - Data Analytics

Role Overview:

This role is for a proactive and agile Technical Lead with a deep understanding of airline data systems. A core requirement of this role is the ability to bridge advanced mathematics, statistical modelling, and aviation business logic.

Operating both as an execution leader and a key individual contributor, you will scale Akasa Air's business intelligence by writing advanced analytical scripts, designing semantic business data schemas, and deploying Machine Learning and AI workflows to transform raw, disparate datasets into predictive data products that support enterprise-wide senior leadership decision-making across Commercial, Operations, Marketing and Finance functions.

Core Responsibilities

1. Technical Leadership 
   1. End-to-End Analytics Delivery: Build, maintain and manage the complete data analytics lifecycle - from exploring raw databases and querying production endpoints to designing semantic analytics schemas and launching executive-ready data insights.
   2. Team Mentorship: Direct, upscale, and manage a team of data analysts across the entire data analytics lifecycle, ensuring best technical practices in SQL coding, Python scripting, and cloud architecture.
   3. Data Architecture: Lead the designing and structuring of enterprise Data Warehouses and Data Marts, defining of optimal schemas that work with large data volumes and evolving business requirements.
2. Cross-Functional Business Collaboration
   1. Cross-Domain Data Correlation: Connect separate airline data streams to identify how operational trends correlate with commercial performance and financial metrics. You will design the semantic data modelling layer, connecting multi-source schemas to deliver unified, cross-functional insights for senior leadership.
   2. Data Governance & Single Source of Truth: Collaborate with business heads across Commercial, Operations, Marketing, and Finance to standardize KPI definitions. Design the unified data logic and semantic layers that eliminate conflicting numbers, ensuring all business teams pull from a single, verified data source.
   3. Analytical Enablement & Partnership: Act as the primary technical bridge for business functions. Partner with business teams to understand their requirements, map their needs to backend tables, and provide the clean datasets and data models they need to run their business strategies.
3. Predictive Modelling & AI Engineering
   1. AI & Advanced Analytics Integration: Infuse AI into traditional analytics workflows, steering your team away from purely reactive reporting toward proactive, predictive, and prescriptive cross-functional data analysis.
   2. Machine Learning Pipelines: Build and deploy time-series forecasting, predictive behavioural modelling, and anomaly detection scripts directly into analytical workflows to transition the airline from static reporting to predictive alerts.
   3. Advanced Analytics & AI Products: Spearhead the development of modern analytics layers, including Robotic Process Automation (RPA) to streamline high-volume tasks, and AI Agents featuring conversational interfaces for standardized KPI definitions and customizable do-it-yourself reporting.

Candidate Profile & Requirements

1. Required Experience & Background 
   1. Total Experience: 8 to 10+ years of progressive technical experience in Advanced Data Analytics, Business Intelligence, or Data Science, with a proven behavioural track record of leading technical teams.
   2. Aviation Expertise: Prior experience working directly with core airline data systems is highly preferred.
   3. Proactive Technical Self-Learner: A relentless technical self-starter with high learning agility who thrives on diving into data deep-ends, uncovers business anomalies before they are explicitly raised by business heads, and independently reverse-engineers unfamiliar legacy system data architectures or poorly documented schemas from the ground up.
   4. Proven Business Impact:

      Looking to get Placed? Try our Placement Guarantee Plan

      Get Hired
      A documented track record of driving cost savings, revenue growth, or massive resource optimizations through data products.

Technical Skill Set:

1. Programming Languages: High proficiency in Python or R heavily focused on data manipulation, advanced statistics, predictive math, and machine learning libraries.
2. Advanced SQL Analytics: Expert-level mastery of advanced SQL syntax (window functions, analytical joins, complex CTEs, data profiling) to manipulate massive relational databases and write semantic logic layers.
3. Cloud Environments: Deep expertise in cloud data architecture, preferably AWS ecosystem tools (e.g., Redshift) or Google Cloud Platform (GCP / BigQuery) alongside orchestration tools like Apache Airflow.
4. Data Visualization: Proven ability to transform raw, multi-source data points into structures optimized for quick, high-impact executive insights.
5. Methodologies: Strong operational knowledge of AGILE frameworks using tools like Atlassian Jira.

Educational Background:

1. Bachelor's degree in an engineering discipline from a premier institution.
2. An MBA or equivalent master's degree from a globally recognized or elite Tier-1 management school (with specialized immersion/exposure to Finance, Data Modelling, System Dynamics, or Operations Research) is strongly preferred.