Technical Project Manager, Tiger Analytics, TX, US

Tiger Analytics is an advanced analytics consulting firm. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our consultants bring deep expertise in Data Science, Machine Learning and AI. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner.

We are looking for someone with a good blend of business consulting skills and a data analytics background. If you are passionate about working on unstructured business problems that can be solved using data, we would like to talk to you.

Responsibilities

• Manage the data analysis and design of analytics products across a focused area of the business value stream to enable data-driven business decisions that will drive performance and lead to the accomplishment of annual goals.
• Translate complex business requirements into data requirements and work with internal customers to define data requirement details based on expressed partner needs.
• Acts as project manager - monitoring the program from initiation through delivery including planning and directing schedules. Gathers input from the project team and acts independently to develop a plan for projects of moderate to significant scope which may include novel products or services.
• Design future state data architectures by applying data requirements, data profiles and business rules to current state architectures in alignment with best practices and standards.
• Drive data architecture that enforces consistency across the data landscape with an emphasis on single sources of truth.
• Perform extensive data profiling to understand data behaviour and business rules.
• Thoroughly document data architecture solutions, business requirements, training material and business processes for both business and technical audiences.
• Drive the adoption of and socialize data products with end users through user training.
• Leverage frameworks for repeatable validation of data integrity within data products supporting accurate and consistent reporting and analytics.
• Support standard methodologies in data analysis: data integrity, unit testing, data quality control, system integration testing, modelling, validation, and documentation.
• Develop SQL queries and data visualizations to fulfill ad-hoc analysis requests and ongoing reporting needs, demonstrating standard query syntax.
• Identify business opportunities, establish goals, and communicate on the analytics pipeline.
• Establishing analytics reporting that enables enhanced decision-making - Dashboard, MIS.
• Provide insights and improve program performance.