Principal Data Engineer - Global Cybersecurity Data & Analytics

Responsible for the analysis, development, and execution of complex data solutions, to manage the information lifecycle needs of an organization. Develops, constructs, tests, and maintains data architectures for data platforms, databases, analytical/reporting, or data science systems. Establishes and builds data systems including providing standards, policies, practice, procedures, governance, and quality assurance for data deliverables. Consults on designing and developing complex programs and tools to support ingestion, curation, and provisioning of complex enterprise data to achieve analytics, reporting, and data science. Builds and designs data architecture that improve accessibility, efficiency, governance, and quality of data. Collects, cleans, and transforms traditional and big data to ensure data accuracy and accessibility. Makes recommendations for how to improve data and drives those recommendations forward. Builds in-depth knowledge of technology enablers. Documents existing processes, organizational structures, architectures, and external systems. Contributes to the development and enhancement of solutions for key business outcomes. Develops interactive and visually appealing data visualizations of large amounts of data to reveal patterns, trends, or correlations. Provides guidance on capabilities and functionalities of Power BI visualization tools. Presents to, and educates, a wide range of people on using visualizations. Actively uses AI tools to improve the speed, quality, and scale of work; can point to concrete workflows where AI adds value; critically evaluates and validates AI-generated output; and applies AI responsibly, protecting confidential and customer data. Bachelor's or Master's degree in a technical or business discipline or equivalent experience, technical degree preferred Generally, 8+ years of professional experience In-depth knowledge of IT concepts, strategies, and methodologies In-depth knowledge of diverse and emerging technologies and new architectural concepts and principles Knowledgeable in data engineering languages and tools; proficient in new and emerging technologies In-depth understanding of layered solutions and designs; in-depth understanding of shared data engineering concepts and product features, as well as security minded In-depth knowledge of business operations, objectives, and strategies; in-depth understanding of global business and technology trends and the financial services industry Highly developed negotiation, consensus building & influencing skills, facilitation, and adaptability to respond to change quickly Highly developed oral and written communication skills; strong presentation skills Ability to effectively collaborate with all levels of the organization Solid Python Pandas and SQL programming skills with analytical and critical thinking skills with an understanding of big data technology An understanding of machine learning principles, statistics and algorithms. Understanding of Data Warehouse solutions, modeling and governance Working knowledge of AWS, Azure and GCP cloud environments Power BI development (DAX and M query) and Power BI gateway Snowflake administration experience AWS Airflow History of working with ETL (extract, transfer and load) and data pipeline technology Proven experience in data modeling, governance and analytics Ability to leverage and build APIs Python programming knowledge SQL query and data management