Manasa Prathi
Building scalable platforms, intelligent workflows, and measurable outcomes at the intersection of data, AI, cloud, and business strategy.
About
I'm a Technical Program and Product Leader with more than a decade of experience turning complex business and technology challenges into scalable platforms, intelligent workflows, and measurable outcomes. My career spans Pinterest, Seagate Technology, Deloitte, Microsoft, and entrepreneurial environments, where I've led programs in data-platform modernization, AI-enabled products, cloud optimization, enterprise transformation, information security, risk management, analytics, and regulatory compliance.
I operate comfortably between executive strategy and engineering depth, translating broad or ambiguous objectives into clear roadmaps, technical requirements, milestones, and measurable success criteria. Whether I'm leading a platform migration, improving data reliability, introducing an AI-enabled workflow, or coordinating a global transformation, my focus stays on creating clarity, building alignment, and delivering sustainable business value.
My leadership approach combines structured execution with curiosity and sound judgment. I'm also actively expanding my hands-on expertise in AI agents and intelligent automation, including AI-assisted root-cause analysis, automated documentation, runbook generation, and human-in-the-loop workflows, with the goal of helping organizations use AI practically and responsibly as a force multiplier for better decisions and faster execution.
What I Bring
- Leadership of complex, multi-team programs from early strategy through launch, adoption, and continuous improvement.
- Technical fluency across data platforms, cloud infrastructure, analytics, APIs, machine learning, security, and enterprise systems.
- The ability to translate between executives, engineers, data scientists, finance teams, security leaders, compliance partners, and business stakeholders.
- Strong program judgment across prioritization, architecture trade-offs, dependencies, risks, resource constraints, and business impact.
- A data-driven approach to defining KPIs, evaluating outcomes, improving operational health, and influencing investment decisions.
- Experience creating operating models, governance mechanisms, documentation standards, and repeatable execution frameworks.
- A continuous-learning mindset focused on AI automation, emerging technologies, and modern program-management practices.
Laurels
- 10+ years leading technical programs and data platforms at Pinterest, Deloitte, Seagate, and Microsoft
- Modernized large-scale data-platform architecture, moving from Hive-based systems to Iceberg to strengthen reliability, scalability, and real-time and machine-learning support
- Improved enterprise risk visibility by redefining security risk tiers, data flows, and system-control mechanisms
- Delivered executive analytics and decision-support capabilities that turned complex operational, financial, and risk data into clear recommendations for leadership
- Led enterprise-scale platform migrations spanning secure data flows, identity management, regulatory requirements, and organizational adoption
- Built alignment across engineering, product, data science, security, finance, risk, compliance, and operations toward shared business outcomes
Skills
Hover or select a category to see everything under it.
Impact
30%
Cut in AWS storage costs through stronger data governance, lifecycle policies, and stale-data management
60%
Improvement in fraud and anomaly-detection accuracy through automated scoring workflows
60%
Reduction in hiring-intelligence processing time through AI-driven data workflows
40%
Increase in adoption of AI-enabled internal tools through user-centered workflow design
30%
Improvement in operational efficiency through Deloitte's Smart Pools data-matching platform
15%
Improvement in resource utilization through Deloitte's Smart Pools allocation capabilities
47+
Locations standardized through a global compliance and controls initiative
Experience

Technical Product Manager · Pinterest
Feb 2025 - PresentLed evolution of Pinterest's large-scale data platform, driving the migration from Hive to Iceberg and partnering with ML engineers on feature signal lifecycle and ingestion pipelines using Spark, Flink, and PyTorch.

Program Manager · Seagate Technology
May 2024 - Jan 2025Redefined InfoSec risk tiers and data flows, optimized the Authorized Device Management System (ADMS), and led data-driven collaboration systems across 47+ global sites.

Senior Consultant · Deloitte
May 2019 - Jul 2023Built the Smart Pools Tool for data ingestion and matching, designed fraud detection and risk-scoring dashboards, and led enterprise-scale HRIS/CRM migrations with a focus on secure data flows and compliance.

Product Owner · SAHAVE Inc
Sep 2017 - May 2019Led product strategy for an AI-powered donation platform focused on transaction legitimacy, managing third-party data integrations and owning the roadmap for data quality and risk modeling.

Business Analyst · Gild, UK
Nov 2016 - May 2017Conducted risk assessments and fraud-resistant onboarding for talent acquisition platforms, leading onshore-offshore Agile teams.

Systems Data Analyst · Microsoft
May 2015 - Nov 2016Led end-to-end business analysis for Microsoft's COSMIC & COMET CRM platforms, spearheading UAT and system enhancements.
Education

Masters · Northern Illinois University, Chicago, US
GPA: 4.0

B.Tech, · JNTU, India
GPA: 3.75
What people say
Recommendations from managers, mentors, and colleagues on LinkedIn.
Key Initiatives
Core Leadership Principles
- Create clarity in ambiguous environments.
- Connect technical decisions to business outcomes.
- Lead through influence, credibility, and sound judgment.
- Use data to inform decisions without losing sight of customer needs.
- Communicate risks and trade-offs early and transparently.
- Build lightweight mechanisms that improve execution at scale.
- Give teams the context needed to make effective decisions.
- Balance speed, quality, security, reliability, and long-term maintainability.
- Treat AI as an operational capability requiring measurement, governance, and human accountability.
- Pursue continuous improvement in products, systems, teams, and personal expertise.

