Senior Data Engineer

Primary responsibilities: • Experience in data wrangling and preparation • Basic knowledge of ETL (Extract, Transform, Load) processes and tools • QA knowledge to identify and remove duplicates • Must be able to research/identify domain-specific data sources • Design meaningful visualization using Qlik, Tableau or other tools. • Be hands-on with data acquisition methods including web scraping • Be able to handle large quantity of data • Be familiar with concepts of data normalization and homogenization • Have a broad understanding of AI/Client models development Nice to have Skills: • Ability to apply a broad range of algorithms to varied data science problems including but not limited to Predictive Analytics, Computer Vision and Text Analytics. • Enhance Data collection procedures to include information that is relevant to building analytics systems. • Python, R, Deep Learning Neural Networks • Traffic Analysis using Computer Vision and AI. • Exposure to Azure AI or AWS Machine Learning Modules a plus. • Ability to write SQL queries as well as logical scripts. Mentoring: Mentors less experienced team members and provides guidance and technical expertise. • Some experience with Analytics/Business Intelligence tools such as Qlik, Birst, Tableau, Power BI, Spotfire, or comparable products. • Ability to create data visualizations and transform concepts into fully realized production applications. • Ability to learn the business and develop relationships that enhance the value of the Business Technology (IT). Ability to manipulate SQL queries. • Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases. • Experience building and optimizing 'big data' data pipelines, architectures and data sets. • Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement. • Strong analytic skills related to working with unstructured datasets. • Build processes supporting data transformation, data structures, metadata, dependency, and workload management. • A successful history of manipulating, processing and extracting value from large, disconnected datasets. • Working knowledge of message queuing, stream processing, and highly scalable 'big data' data stores. • Strong project management and organizational skills. • Experience supporting and working with cross-functional teams in a dynamic environment