Lead Data Engineer

7+ years of experience in data engineering, with at least 2 years in a technical leadership role Strong proficiency in Python and SQL for building production data pipelines and data transformations Strong experience in designing, building, and maintaining cloud-based data pipelines using orchestration tools such as Airflow, Dagster, Prefect, Temporal, or similar Hands-on experience with cloud data platforms (AWS, GCP, or Azure) and modern data stack tools Deep understanding of data warehousing concepts and experience with platforms like Snowflake, BigQuery, Redshift, or similar Strong software engineering fundamentals including testing, CI/CD, version control, and writing maintainable, documented code Track record of optimizing data systems for performance, reliability, and cost efficiency at scale Excellent communication skills and ability to collaborate with cross-functional teams

Design and build scalable data pipelines to ingest, process, and transform data at high volume and low latency Architect and maintain data infrastructure for real-time analytics, P&L calculations, leaderboards, market cap tracking, and liquidity monitoring Own ETL/ELT processes to transform raw onchain data into reliable datasets Build and optimize data models and schemas for operational and analytical use cases Establish data quality frameworks including monitoring, alerting, testing, and validation Collaborate with backend engineers on event schemas, data contracts, and APIs Partner with product and analytics teams to translate data needs into engineering solutions Provide technical leadership by mentoring engineers, conducting code reviews, establishing best practices, and driving architectural decisions Optimize performance and costs of data infrastructure