Skills

  • XFN Management: Managing cross-functional teams and relationships within an organization.
  • Scrum: An agile framework for managing and delivering complex projects, emphasizing collaboration, adaptability, and iterative development.
  • Stakeholder/Customer Management: Engaging and managing relationships with stakeholders and customers to ensure their needs are understood and met.
  • Metric Generation/Presentation: Creating and presenting meaningful metrics and data visualizations to track performance and support decision-making.
    • Based on product or program goals, define metrics to evaluate the success of the program and or product and other objectives and track as OKR's. Sample metrics:

      • Success rate - % of transactions successfully completed

      • Failure rate - % of transactions that failed/encountered errors

      • Time to complete transactions - average time it takes to complete a booking

      • User satisfaction score - collected via surveys or NPS

      • User retention rate - The percentage of users who continue to use your product actively over a given period of time. For example, 1-month retention would be the % of users active after 1 month.

      • Churn rate - The inverse of retention, this is the % of users who stopped using your product in a given time period. Lower churn is better.

      • Repeat usage rate - The % of users who use your product multiple times within a given timeframe. Higher repeat usage indicates better retention.

      • Active users - The number or % of users who actively used your product in the past 7, 30 or 90 days. Comparing active users over time indicates retention trend.

      • Reactivation rate - Of inactive users, the % that come back and re-engage with your product after a period of inactivity. Higher is better.

      • User lifecycle - The average lifespan of a user before they churn. Longer lifecycle equals better retention.

      • Cohort analysis - Tracking retention rates of specific user cohorts over time. Comparing cohorts shows if newer users retain better.

      • Clickthrough rate (CTR) - The percentage of times a user clicks on a specific link, content piece, or feature divided by the number of impressions. Higher CTR indicates higher relevance.

      • Bounce rate - The percentage of users who land on a page and then leave without any further interaction. Lower bounce rates suggest content is more relevant.

      • Time on page - The amount of time users spend on a particular page. More time spent indicates higher relevance.

      • Search success rate - For products with search, this is the % of searches that produce relevant results. Higher is better.

      • Abandonment rate - The % of users who abandon certain workflows. Lower abandonment equates to higher relevance.

      • Sentiment analysis - Natural language processing to detect if user feedback indicates positive or negative sentiment. More positive sentiment implies higher relevance.

      • Net Promoter Score (NPS) - High NPS scores suggest higher user satisfaction and relevance.

      • Surveys - Directly asking users to rate the relevance of features, content or recommendations on a 1-5 scale. Higher is better.

      • A/B testing and holdouts - Trying variations of content or features and measuring which performs better on key metrics like CTR.

  • Resource Management: Efficiently allocating and managing resources, such as personnel, budget, and equipment, to optimize project outcomes.
  • Program Management: Overseeing multiple related projects within a program, ensuring coordination, alignment, and successful delivery.
  • Data Analysis: Analyzing and interpreting data to extract insights, identify patterns, and make informed decisions.
  • Product Lifecycle Management: Managing the entire lifecycle of a product, from ideation and development to launch, maintenance, and eventual retirement.
  • Cybersecurity/Identity & Access Management: Protecting computer systems and data from unauthorized access and managing user identities, roles, and access permissions.
  • PM Tools (SmartSheets, Wrike, MS Project): Project management tools and software used for planning, scheduling, and tracking project progress and tasks.
  • Cybersecurity Standards (NIST): Guidelines and best practices for enhancing cybersecurity, developed by the National Institute of Standards and Technology (NIST).
  • Financial Risk, Controls, and Compliance: Managing and mitigating financial risks, implementing controls, and ensuring compliance with regulatory requirements.
  • Reliability and Observability: Ensuring the stability, availability, and performance of systems and applications, and gaining insights into their behavior.
  • Linux/CentOS/Debian CLI/Bash: Operating systems and command-line interfaces (CLI) used in the Linux environment, with Bash being a popular Unix shell and scripting language.
  • Apache/Web/HTML: Apache refers to the Apache Software Foundation, which develops and maintains open-source projects. Web and HTML refer to technologies and languages used for building and accessing websites.
  • NOSQL: Non-relational databases that provide flexible data storage solutions beyond traditional relational databases.
  • Cloud Tech/Kubernetes/SaaS/PaaS: Cloud technologies and services that enable scalable and flexible computing and storage, with Kubernetes being an open-source container orchestration platform, and SaaS (Software as a Service) and PaaS (Platform as a Service) representing cloud-based software and platform offerings.
  • CI/CD: Continuous Integration (CI) and Continuous Deployment (CD) practices for automating and streamlining software development, testing, and deployment processes.
  • Change Mgmt/Git: Managing and implementing changes to systems and processes, with Git being a distributed version control system used for tracking and managing source code changes.
  • Automation/RPA/Alteryx: Automation technologies, including Robotic Process Automation (RPA), used to automate repetitive tasks and streamline workflows, with Alteryx being a data analytics and automation platform.
  • ETL/Spark: Extract, Transform, Load (ETL) refers to the process of extracting data from various sources, transforming it, and loading it into a target system. Apache Spark is a fast and distributed data processing engine commonly used for big data analytics.
  • SQL/Oracle/MySQL: SQL is a standard language for managing and manipulating relational databases. Oracle and MySQL are popular relational database management systems.
  • Scuba/Presto: Scuba is a distributed data store and analytics platform for real-time analysis of large-scale structured data. Presto is a distributed SQL query engine for running interactive analytics queries against large-scale data sources.
  • ML/AI/DS: Machine Learning (ML), Artificial Intelligence (AI), and Data Science (DS) involve developing and applying algorithms and techniques to analyze and extract insights from data.
  • Splunk: A software platform used for searching, monitoring, and analyzing machine-generated big data to provide operational intelligence and security information.
  • Bash: A Unix shell and scripting language used for automating tasks and running command-line operations.
  • JavaScript/TypeScript: JavaScript is a programming language widely used for web development, while TypeScript is a typed superset of JavaScript that adds static typing and other features for enhanced development.
  • Java: A popular general-purpose programming language known for its platform independence and extensive libraries.
  • Python: A high-level programming language known for its simplicity, readability, and versatility, widely used in various domains including web development, data analysis, and automation.
  • Microservices/Spring Boot: Architectural style and framework for building modular and scalable applications by breaking them down into smaller, independent services. Spring Boot is a popular Java-based framework for developing microservices.
  • APIs/REST/Thrift/Oauth/S3/EC2: Technologies and protocols used for building and accessing web services, including RESTful APIs, data serialization (Thrift), authentication (OAuth), and cloud storage and computing services (S3, EC2).
  • Data Engineering/Data Pipelines: The processes and systems used to extract, transform, and load data, enabling efficient data flow and analysis.
  • GraphQL: A query language for APIs that provides a flexible and efficient approach to fetching and manipulating data.
  • CUDA: A parallel computing platform and API that enables efficient GPU programming for accelerated computing tasks.
  • React: A JavaScript library for building user interfaces, often used for creating dynamic and interactive web applications.
  • VSCode: Visual Studio Code, a popular source code editor with advanced features and extensibility.
  • Torch/Tensor/Etc: Frameworks and libraries for machine learning and deep learning.