Technical Product Manager

Amay Shah

Dynamic Technical Product Manager and Technical Project Manager with a strong foundational background in Computer Science and advanced certifications including PMP, AWS AI Practitioner, and FinOps Practitioner/FOCUS Analyst. I possess extensive experience steering the end-to-end SDLC for cloud governance, machine learning, mobile/web SDK products, and third-party developer integrations, as well as AI-powered FinOps platforms—including cost recommendation engines, forecasting models, and conversational AI chatbots.

As a proven roadmap owner, I am adept at authoring detailed PRDs, managing backlogs, and leading cross-functional teams of 40+ engineers and data scientists across concurrent Agile, Scrum, and Kanban workstreams. I excel at managing SDK release schedules, orchestrating zero-downtime production deployments, and aligning engineering capacity with strategic business priorities to deliver scalable, data-driven solutions.

☁️ Cloud FinOps🧠 AI/ML Products🚀 Agile/Scrum/Kanban Delivery📈 Roadmap Ownership🔌 Third-Party Developer Integrations🛠️ SDK Delivery, CI/CD & Release Management

Phoenix, AZ, USAamay.softwaredev@gmail.com LinkedIn GitHub

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40+

Engineers Led

Across 4 concurrent teams

Releases / Month

Zero-downtime deployments

20%

Data Integrity ↑

AI/ML validation gains

25%

Downtime ↓

Automated error handling

1-Wk

Sprint Cadence

Applied Agile & Scrum

100%

QA Validation

Rigorous UAT sign-off

Customers

CSM & partner alignment

Multi-Cloud

FinOps Governance

AWS, Azure & GCP optimization

Career

Experience

Technical Project Manager — Cloud FinOps

Finomics·Hybrid·Chandler, AZ

Sept 2025 – Present

40+Engineers

4+Releases/Month

20%Integrity ↑

Own the product roadmap for AI/ML-powered FinOps products, collaborating with Product Management to translate roadmap priorities into actionable project plans, detailed user stories, and clear Definitions of Done.
Lead collaborative discovery sessions with client stakeholders to analyze workflows, mapping complex requirements directly onto the AI/ML FinOps product roadmap to ensure custom features drive broad platform value.
Act as the primary conduit between key enterprise clients and the engineering team, capturing ad-hoc requests and translating vague business pain points into functional design mockups and clear technical feature specifications.
Establish a structured feedback loop that screens, prioritizes, and scopes client-requested enhancements, converting custom requests into scalable, repeatable platform features that accelerate customer onboarding.
Plan and facilitate fast-paced, one-week sprint cycles, kickoff meetings, daily standups, and retrospectives for 40+ engineers and QA across four concurrent global teams, optimizing delivery cadence.
Manage release management, SDK release schedules, and CI/CD pipeline workflows with engineering, communicating timelines clearly to internal stakeholders and ensuring smooth, zero-downtime production deployments.
Monitor delivery risks and proactively remove blockers to keep sprints on target while optimizing team velocity and tracking story points.
Conduct competitive analysis and market research to inform feature prioritization, go-to-market strategy, feature design, and user experience reviews.
Execute rigorous UAT/production validation testing for AI/ML features, resulting in a 20% improvement in data integrity and more predictable deployment cycles.
Conduct technical product demonstrations for multiple resellers and external partners, effectively translating between complex technical concepts and business stakeholders to drive engagement.

AI/MLFinOpsAgileCI/CDAWSPRDs

Technical Project Manager — Enterprise Data Warehouse

IRCC — Gov. of Canada·Remote·Ottawa, CA

May 2024 – Aug 2025

25%Downtime ↓

20%Integrity ↑

Led a cross-functional team through the full SDLC to automate weekly Netezza server refreshes, defining project scope and timelines using Kanban boards and maintaining comprehensive documentation.
Partnered with database administrators, security teams, and business analysts to align the automated server refresh product with broader enterprise infrastructure goals.
Managed project risk and quality control with automated error-handling and standardized load-count tracking, proactively removing blockers to reduce downtime by 25% and improve data integrity by 20%.
Monitored system performance metrics and user feedback post-launch to identify bottlenecks, iteratively prioritizing the backlog to improve system reliability and the overall internal user experience.
Authored comprehensive product documentation and compliance frameworks, ensuring all automated data workflows met strict government data governance, privacy, and compliance standards.
Utilized strong written and verbal communication skills to manage remote project coordination and facilitate regular sprint reviews across remote environments.

SDLCKanbanGovernmentData Engineering

Technical Project Manager

Amay Exim Inc.·Remote·Toronto, CA

May 2021 – Aug 2024

5+Teams

3+Markets

Oversaw the end-to-end delivery of software products and third-party integration workflows for a machine learning project, ensuring technology reached partners on time and according to specifications.
Defined the Minimum Viable Product (MVP) scope for the machine learning optimization tool, using rigorous feature prioritization to deliver rapid business value while minimizing initial development costs.
Coordinated project activities with cross-functional teams across North America and Europe, managing dependencies at the intersection of engineering, data science, product teams, and external partnerships.
Managed backlog prioritization and sprint cadence using a hybrid methodology while leveraging project tools to run seamless remote collaboration across multiple time zones.
Managed the full product lifecycle of internal data tools, seamlessly sunsetting manual spreadsheet workflows and transitioning the global procurement team to automated, predictive supply-chain models.
Conducted user feedback interviews with internal sales and procurement teams to overhaul internal reporting dashboards, significantly improving data readability and cross-department adoption.
Performed market analysis across international markets to shape product recommendations and reduce procurement costs, building a technical background well-suited for location services or IoT platforms.

MLAgile-WaterfallMarket AnalysisKPIs

Expertise

PM Metrics Dashboard

Cloud Platforms

AWS · Azure · GCP · 21Vianet (Azure China)

Methodologies

Agile · Scrum · Kanban · Hybrid · SDLC · CI/CD · Release Management · SDK Delivery

PM Competencies

Sprint Planning · Backlog Grooming · Feature Prioritization · Scope · Risk · Stakeholder · Resource · Competitive Analysis

10+

PM Tools

Jira · Azure DevOps · MS Project · Confluence · Power BI · SmartSheets · Excel · Google Analytics · Tableau

10+

Cloud Services

S3 · EC2 · Comprehend · Q Business · IAM · QuickSight · Azure DevOps · Bedrock · Textract

Databases

MySQL · Oracle · NoSQL · Victoria Metrics · PostgreSQL · Netezza · DynamoDB · Amazon RDS

Academic

Education

Toronto Metropolitan University

2018 – 2025

BSc (Hons) — Computer Science

Minor: Information Technology Management · Toronto, CA

Big Data SystemsData ScienceData MiningDBMSComputer NetworksAlgorithmsStatisticsLinear Algebra

Portfolio

Featured Projects

Medical Doc Analyzer

AWS NLP Pipeline

Architected and engineered an end-to-end NLP data pipeline on AWS to analyze medical datasets using Amazon Comprehend.
Automated data ingestion workflows by uploading datasets to Amazon S3 via the AWS CLI, processing and structuring the outputs into standardized JSON payloads, and storing results in DynamoDB for low-latency querying.
Maintained full data dictionary documentation and designed the schema-less storage layer to ensure efficient querying and seamless data retrieval by downstream analytics teams.
Drafted strict documentation for data security and governance, implementing secure IAM inline policies following the principle of least-privilege access alongside mandatory S3 bucket encryption and object versioning for project isolation.

PythonAmazon S3Amazon ComprehendAmazon DynamoDBIAM

AWS-Powered Global Compliance Chatbot

Generative AI & RAG

Authored a comprehensive Product Requirement Document (PRD) and defined the feature scope to build a compliance chatbot utilizing Retrieval-Augmented Generation (RAG) through Amazon Q Business to deliver country-specific forestry regulation insights.
Designed the underlying data ingestion architecture, utilizing Amazon S3 as a scalable data lake to centralize, version, and manage cross-border compliance documentation.
Mapped out user persona workflows to optimize chatbot prompt accuracy, enabling employees across continents to instantly query multi-country regulations and drastically reduce manual research time.
Created detailed system administration documentation, mapping content-source connectors and data access controls to ensure the chatbot strictly surfaces information based on defined user authorization levels.

Amazon Q BusinessAmazon S3AI/MLRAG

ETL Pipeline with PySpark API and HDFS

Big Data Engineering

Developed a comprehensive technical specification layout to build a scalable, end-to-end ETL pipeline, configuring a multi-node Apache Hadoop cluster on Oracle Virtual Machines to process large-scale wood product datasets.
Engineered robust PySpark transformation logic to convert raw datasets into highly optimized, compressed Apache Parquet columnar formats within HDFS.
Authored performance optimization runbooks analyzing formats, custom data partitioning strategies, schema enforcement rules, and access patterns to drastically improve query performance and minimize storage costs.
Built technical documentation and performance dashboards to track, analyze, and communicate pipeline efficiency metrics, compute utilization, and data integrity milestones.

PythonApache HadoopHDFSApache SparkSQLOracle VM

Obesity Projection Analysis

Data Mining & Machine Learning

Managed the full data lifecycle for a predictive machine learning model to estimate predictions in the Obesity dataset, from establishing initial evaluation metrics to executing feature prioritization and selecting core algorithms.
Spearheaded data preprocessing workflows, feature selection, missing value imputation, and Principal Component Analysis (PCA) to streamline high-dimensional datasets while documenting feature importance scores.
Orchestrated rigorous model validation across various regression and classification algorithms, thoroughly documenting and achieving high accuracy with a Random Forest Regressor framework.
Leveraged Python libraries including Matplotlib, NumPy, and Pandas to design advanced, management-level data visualizations and analytical reports, translating complex statistical model outputs into clear, intuitive insights.

PythonPandasNumPyMatplotlibScikit-Learn

Credentials