• Technical leadership and guidance of a team of infrastructure and data engineers • Strategic development and refinement of a forward-looking Data Mesh architecture • Managing data platform scaling and ensuring structural integrity within the cloud ecosystem • Close collaboration with cross-functional teams to support data migrations toward a modern Lakehouse environment

• Use your expertise to drive data and AI architecture and technology decisions • Provide insights that influence the overall strategy and plan for a scalable data solution or product that meets business needs • Educate customers on how to craft highly scalable, flexible, resilient, secure, and cost-effective architectures • Link technology with measurable business value and outcome • Identify and investigate new tools and technologies • Translate client needs into digestible requirements

• Dive deep into blockchain data (Ethereum and other L1/L2s) and enrich it with insights from various data providers. • Build pipelines and infrastructure to power a variety of analytics and dashboards. • Work across teams, design new features, and take ownership of sourcing, verifying, and maintaining high-quality data.

• Lead and provide expert support for data collection, data validation, data visualization, and analytics initiatives • Apply disciplined methodologies for the planning, analysis, design, and development of information systems on an enterprise-wide basis or across a business sector • Develop analytical techniques and methodologies to solve complex business and technical problems • Perform strategic systems planning, business information planning, and business analysis • Organize and analyze large volumes of structured and unstructured data sets using data analytical tools • Locate, access, merge, clean, and standardize data from multiple sources, and develop derived metrics • Create and implement data collection and analysis tools using programming languages such as Python, Databricks, SQL, Scala, R, and Java • Design, script, debug, and analyze data engineering solutions • Implement and create machine learning-based tools and processes • Apply distributed and parallel processing technologies (e.g., Spark) to handle big data analytics tasks involving large data volumes • Perform SQL Server data imports from CSV and TXT files • Leverage Excel and Google Suite for data analysis, reporting, and collaboration • Utilize supporting tools and platforms such as Pentaho (data import/transformation), Azure Data Studio, GitHub, and Smartsheet as needed • Document task requirements, work completed, processes, and technical details thoroughly • Communicate effectively with stakeholders across technical and business teams • Operate independently as a subject matter expert in a fast-paced, entrepreneurial environment