Overview We are looking for seasoned Data Technical Lead to work with our team and our clients to develop enterprise grade data platforms, services, pipelines, data models, visualizations, and more! The Tech Lead needs to be a technologist with excellent communication and customer service skills and a passion for data and problem solving. This role involves technical and thought leadership across the spectrum of data capabilities, from data vision and strategy all the way through data science. Contributions Designing and leading the implementation of greenfield data solution stacks in the cloud or on premises, using the latest data services, products, technology, and industry best practices Expand upon existing models to estimate how long it will take clients to process digital forms/applications and to present relevant information to users about their application processing journey Develop and deploy personalized Application Processing Time predictive models for additional services/forms as prioritized by client Add integration points for intelligent automated help agent to connect with external applications and/or data sources, to increase the agent’s effectiveness and accuracy Create and implement data cleaning and processing strategies to ensure unstructured data is appropriate to present to the end user/customer Analyze customer data and biometrics to identify key data points for confirming a customer’s identity Design and implement data acquisition pipelines to ensure clean structured, and high-quality data ingestion from a web application with form-based inputs, applying validation, transformation, and deduplication techniques to enhance data integrity Develop scalable data integration solutions by enabling real-time data sharing across enterprise systems using technologies (e.g., Kafka, AWS Kinesis, oar Apache Pulsar), ensuring seamless interoperability and adherence to data governance standards Data Architecture contributions include assessing, understanding, and implementing data sources, data models and schemas, and data workflows Data Engineering contributions include assessing, understanding, designing, and implementing ETL jobs, data pipelines, and workflows Analyze, validate, and create an ETL process to transition from a transaction-based model to a customer-centric model Data Science contributions include assessing, understanding, designing, and implementing machine learning and AI applications, designing MLOps pipelines, and supporting data scientists Addressing technical inquiries concerning customization, integration, enterprise architecture and general feature / functionality of data products Enhance chatbot intelligence and user experience by refining LLM and natural language processing (NLP) models, improving response accuracy, and optimizing conversational flows using machine learning techniques and user interaction data Support refactoring and optimizing chatbot architecture to improve scalability, efficiency, and maintainability , leveraging advanced AI/ML techniques, integrating with modern data pipelines, and implementing robust monitoring and analytics for continuous improvement Key must have skill sets – broad understanding of data exploitation lifecycle and capabilities, technical leadership in the data field Support an Agile software development lifecycle