GTM Data Engineering Lead, dYdX

• Bachelor’s or Master’s degree in Computer Science, Engineering, Statistics, or a related field, or equivalent experience
• 4+ years of experience in data engineering, data analytics, or a similar discipline
• 2+ years of experience working with blockchain or order book data (e.g. experience indexing blocks, building dashboards in Dune or similar, working with market data from crypto exchanges)
• Expertise in SQL, database technologies, cloud databases, and reporting technologies (BigQuery, GCP, and Mode or similar)
• Proficient Python, ability to read and reason about the dYdX protocol (Go) and indexing (TypeScript) code
• Familiarity with data visualization tools for reporting and insights
• Solid understanding of ETL processes, data modeling, and data warehousing principles
• Strong problem-solving skills with a keen attention to detail
• Effective communication and collaboration skills
• Excellent project management and multitasking abilities
• Curiosity-driven mindset, focused on asking questions, understanding data, and forming hypotheses

Desirable

• Design and maintain scalable data pipelines and well-structured data models in our data warehouse (e.g., using SQL) to support Mode dashboards, reporting, and self-serve analytics for marketing and engineering teams
• Design data pipelines for blockchain transaction, order book and market participant data, support analyses and alerting related to market activity and product usage
• Ensure high data quality and integrity by implementing robust validation, monitoring, and alerting systems, along with clear documentation and data governance practices
• Collaborate closely with marketing, engineering and other crossfunctional teams to understand campaign strategies, lifecycle efforts, product usage metrics, and translate those into actionable data insights using tools like SQL and Mode
• Support growth experimentation and performance analysis by helping track new campaigns, run A/B tests, analyze user cohorts, and surface insights to guide strategic product decisions
• Help instill a data-first culture by making tools and metrics intuitive, reliable, and accessible. Provide guidance to teammates on how to use and interpret data
• Continuously improve the data infrastructure by exploring new tools, optimizing existing systems for performance and scalability, and driving adoption of data best practices across the organization
• Work closely with cross-functional teams to understand data requirements and deliver solutions that meet business needs