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TECHNICAL BUSINESS ANALYST

SUSHANK SHARMA

BUSINESS SYSTEMS, DATA & AVIATION

Requirements Engineering | API Contract Validation | Data-Driven Decision Support | AI-Assisted Analysis | Agile Delivery

Key Business Outcomes

  • 35% fewer requirement defects
  • 25% faster UAT sign‑off
  • 35% fewer cross‑team clarification calls
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Professional Summary

Technical Business Analyst with 2.5 years of dedicated BA experience on an enterprise compliance platform (DGCA‑regulated, 6,000+ users). Built a technical foundation through internships in web development and data operations, then deliberately upskilled in business analysis, SQL, and Agile before joining IndiGo's Training Management System team. Known for translating regulatory workflows into precise user stories, accelerating UAT through early stakeholder walkthroughs, and using SQL/API contract validation to verify requirements — leading to 35% fewer requirement defects, 25% faster UAT sign‑off, and 35% fewer cross‑team clarification calls. Recognised by stakeholders for bridging business needs and technical delivery. Immediate joiner.

Education & Professional Development

  • M.E., Electronics & Communication – Manipal Academy of Higher Education (MAHE), CGPA: 8.68, 2022
  • B.E., Electronics & Communication – Visvesvaraya Technological University (VTU), 2020

Professional Development (Feb 2023 – Feb 2024):

  • Completed intensive training in Core Java, SQL, Web Technologies, and J2EE at Java Development Training & Placement Institute (Jan 2024), strengthening the technical base needed for a Technical BA role.
  • Self‑studied Agile frameworks (Scrum, Kanban) and generative AI applications for business analysis workflows.

Certifications

IBM Business Analysis Foundations (Coursera) | Google Data Analytics Foundations (Coursera) | Scrum Fundamentals & Agile Practices (SkillUp)

Technical Business Analysis Capabilities

  • Requirements & Analysis: Elicitation (workshops, interviews), User Stories & Acceptance Criteria, Process Modelling, Gap Analysis, Impact Assessment, MoSCoW Prioritisation, Root Cause Analysis (business process)
  • Agile Delivery: Sprint Planning, Backlog Refinement, Daily Stand‑ups, Sprint Reviews, Retrospectives, User Story Mapping, Definition of Done, Distributed Team Collaboration
  • Technical Validation: SQL (intermediate — joins, aggregations, data quality checks), API Contract Validation (REST/SOAP), Excel (Power Query, PivotTables, VLOOKUP, conditional formatting), Python (data exploration with Pandas), UAT Coordination
  • Domain & Compliance: Aviation Regulatory Frameworks (DGCA/EASA Audit Readiness), Training Management Systems, Certification Workflows, Controlled Document Management
  • Tools & AI: Jira, Confluence, Generative AI for BA tasks (ChatGPT, Copilot), Cloud Concepts (exposure to AWS/GCP through IoT internship)

Professional Experience

Business Analyst @ Anugraha Exceed Pvt. Ltd. (Client: InterGlobe Aviation / IndiGo)

Mar 2024 – Jun 2026  |  Gurugram
  • Elicited and documented requirements for an enterprise platform used by 6,000+ aviation professionals. Facilitated 12 stakeholder workshops to break down DGCA‑mandated workflows into 20+ user stories per quarter, each with acceptance criteria traceable to regulatory clauses. Reduced cross‑team clarification calls by 35% (measured by decline in ad‑hoc queries via Teams Internal Support Group post‑workshop).
  • Maintained a prioritised backlog of 20–50 items; applied MoSCoW and value‑vs‑effort scoring to help the Product Owner sequence features, contributing to a 35% reduction in requirement‑related defects (tracked in Jira against stories with explicit acceptance criteria vs. prior baseline).
  • Coordinated UAT for 25+ release cycles, translating technical defects into business impact summaries. Introduced early stakeholder walkthroughs of acceptance criteria, cutting UAT sign‑off time by 25% (tracked from "Ready for UAT" to "Accepted").
  • Validated data migrations and business rules using SQL queries, catching inconsistencies pre‑production; verified API integration points against functional specs using contract validation, preventing downstream regressions.
  • Leveraged generative AI (ChatGPT, Copilot) to draft user stories, acceptance criteria, and meeting summaries, significantly reducing documentation effort; conducted team knowledge‑sharing sessions.

Analytical Project Portfolio

Three evidence-led case studies spanning operational analytics, API integration, and controlled experimentation.

Operational Analytics

TMS Training Insights Dashboard

A self-service decision-support prototype for exploring training operations. It demonstrates how a Technical BA can turn fragmented operational records into agreed KPIs, filterable views, and exportable evidence for stakeholder validation.

What this demonstrates
  • Transforms monthly JSON extracts into analysis-ready curriculum, lesson, instructor, and trainee tables
  • Calculates eight KPIs and supports date, duty-code, curriculum, and lesson filters
  • Provides searchable drill-downs, charts, and Excel export for stakeholder review
  • Python
  • Streamlit
  • Pandas
  • Plotly
  • Excel
GitHubRepository

API & System Integration

Automotive Navigation Integration Prototype

A requirements-led navigation prototype that connects mapping services with user journeys such as saved locations, recent searches, travel history, and faster-route prompts. It demonstrates translating integration behaviour and platform constraints into a usable product workflow.

What this demonstrates
  • Integrates directions, geocoding, distance, and traffic services through Google Maps APIs
  • Models saved-location, recent-search, travel-history, and rerouting workflow states
  • Captures platform constraints and sprint decisions in a cross-functional delivery context
  • React
  • Google Maps APIs
  • WebSocket
  • Geolocation
  • JSON
GitHubRepository

Analytical Experimentation

Histopathology Model Evaluation

An academic proof of concept using histopathology images to demonstrate data preparation, controlled comparison, and model evaluation. This is an analytical learning project, not a medical diagnostic product.

What this demonstrates
  • Structures train, validation, and test datasets for repeatable analysis
  • Compares image preprocessing, normalisation, and augmentation choices
  • Evaluates a VGG16 transfer-learning proof of concept with an explicit non-diagnostic scope
  • Python
  • Jupyter
  • TensorFlow
  • Keras
  • OpenCV
  • VGG16

What's Next?

Get In Touch

Gurugram, India | Immediate Joiner | Remote-Ready | Global Project Exposure

+91 99008 41182 | sushanks07@gmail.com

LinkedIn | GitHub | Portfolio | Recommendations

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