Megha Kanojia

• • Analytics Dashboard Development: Designed and maintained Power BI and Adobe Analytics dashboards to track KPIs across 500+ users, automating financial and operational reporting workflows that reduced manual effort by 30%.

• • Data-Driven Decision Support: Acted as technical lead for 100+ teams, translating business use cases into data collection and analytics strategies, delivering actionable insights to senior leadership through compelling data storytelling.

• • Pipeline Automation & Process Improvement: Implemented Python and Linux automation scripts to trigger alerts and auto-restart critical analytics processes, achieving 99% service uptime and significantly improving operational efficiency.

• • Cloud Migration & Cost Reduction: Led migration of enterprise sites to AEM Cloud Service (AEMaaCS), reducing infrastructure costs by ~15% while ensuring 99.9% application parity, demonstrating finance relevant cost-benefit analysis in practice.

• • Data Governance & Compliance: Administered Adobe Experience Cloud via RBAC (Role-Based Access Control), enforcing data security protocols and ensuring reliable, compliant data delivery to analysts and scientists.

• • Cross-Functional Collaboration: Partnered with finance, analytics, and technology teams across global time zones; presented data insights and platform updates to senior stakeholders and large user groups.

• • Managed enterprise analytics stack (Adobe Analytics, Power BI, Angelfish) for 500+ users; leveraged automation to reduce support latency by 30% and maintain 99.99% uptime.

• • Spearheaded training for 4+ junior engineers and developed automated onboarding workflows, cutting ramp up time by 40% while ensuring 100% adherence to security and compliance protocols.

• • Engineered an automated data validation pipeline for 200K+ records using Python (Pandas/NumPy), identifying 15% missing value gaps to ensure 99% data integrity for downstream financial modelling.

• • Built 5+ interactive diagnostic visualizations using Matplotlib and Seaborn to communicate feature importance and model insights to stakeholders, supporting data-driven decisions.

• • Optimized predictive model performance across 10+ algorithms (Random Forest, XGBoost) via Grid Search CV, achieving a 12% increase in F1 -score, demonstrating AI/ML application to complex analytical problems.