• Develop, debug and support ETL processes utilizing AWS services • Lead ideation and development of data models used for data science and analytics • Meet the delivery expectations of the Agile Project Management methodology • Maintain and optimize reports and extracts that serve as lifesaving information sources to customers • Create clear and concise documentation regarding technical solutions, while sharing knowledge and documentation with teammates via “Lunch and Learns” • Collaborate with internal and external customers to deliver modern data products • Explore opportunities to enhance workflows through AI or automation tools (e.g., document summarization, task routing, or data parsing) • Identify repetitive tasks and partner with team leads to implement scalable automation solutions

• You will serve two primary functions: (1) designing, building, and maintaining scalable data pipelines and infrastructure to help the Economics and Executive Leadership teams make informed, data-driven decisions, and (2) building and maintaining production economics applications. • You will work on a wide array of blockchain economics projects, analyses, and systems, including: • Designing novel mechanisms and incentive schemes • Building data pipelines, models, and simulations to evaluate investment opportunities • Designing and parameterizing economics systems • Optimizing parameters for production applications • Contributing to the design of Economics programs and applications • You will optimize network incentives and systems by engaging directly with the Economics, Product, Engineering, Research, and Data Science teams to understand network dynamics at the deepest level. • You will meticulously operationalize processes, and coordinate cross-functionally to implement them.

• Define and own the architecture for cloud-based data solutions, ensuring scalability, reliability, and high availability • Lead data architecture discussions and collaborate closely with engineering, product, ML, and cross-functional stakeholders • Design, build, and maintain production datastores supporting high-speed, high-volume industrial IoT and telematics data • Translate business and product requirements into conceptual, logical, and physical data models • Drive best practices for data reliability, performance tuning, partitioning, indexing, and data quality • Analyze how customers and systems use data, identify inefficiencies, and propose architectural improvements • Coach and mentor engineers on data architecture best practices through design reviews, code reviews, and technical guidance • Clearly explain complex technical solutions to both technical and non-technical audiences, including leadership

• Lead the design and implementation of large-scale data architectures for cloud-based systems (AWS, Azure) to efficiently ingest, store, and process massive volumes of security telemetry and alerts • Spearhead advanced AI/ML initiatives, including Generative AI, to develop end-to-end AI solutions for SOC automation, threat detection, and threat hunting, leveraging frameworks like Scikit-learn, TensorFlow, and PyTorch • Drive the use of Large Language Models (LLMs) and AI Agents to enhance the enrichment of security data, enabling faster human decision-making, while exploring and evaluating various LLM architectures • Collaborate across teams to integrate ML-driven insights into the platform and apply automation and analytics to reduce analyst workload and enhance detection fidelity • Provide architectural guidance across engineering based on the fast-paced world of GenAI, Agents, and classic ML models, including those developed by our internal R&D teams