Lead Data Engineer, Figment

• Experience with the data transformation tool DBT
• Having led the design and implementation of complex data transformations using advanced DBT models, materializations, and configurations to streamline data workflows and improve performance
• Having led the optimization and troubleshooting of DBT pipelines for scale, ensuring that transformations run efficiently in production environments, handling large datasets without issues
• Experience programming in Python
• Having led the design and implementation of scalable, high-performance applications by leveraging Python's advanced libraries and frameworks (e.g., Pandas, FastAPI, asyncio), ensuring clean code, modularity, and maintainability
• Having led the optimization of code for performance and memory usage through profiling and refactoring, ensuring efficient execution, particularly when handling large datasets or real-time processing tasks
• Experience with the data orchestration tool - Dagster or Airflow
• Having led the design and orchestration of complex DAGs to manage dependencies, triggers, and retries for data workflows, ensuring reliable and efficient pipeline execution
• Having led the implementation of monitoring and alerting for pipeline failures or performance bottlenecks, using observability tools integrated with Dagster/Airflow to maintain robust pipeline health
• Experience with a data warehousing solution such as Snowflake, BigQuery, Redshift, or Databricks
• Having led architecting and optimizing warehouse environments for performance, including designing partitioning strategies, clustering keys, and storage optimizations for cost-effective scaling
• Having led the implementation of security and governance policies within the wareshouse, including data encryption, access control, and audit logging to meet compliance and security best practices
• Extensive data engineering experience, including building and managing data pipelines and ETL processes
• Experience developing CI/CD pipelines for automated data infrastructure provisioning and application deployment
• Experience in managing infrastructure across AWS, with a focus on performance and security

Desirable

• Lead the design and implementation of reliable data pipelines and data storage solutions
• Lead the implementation of data modeling and integrate technologies according to project needs
• Manage specific data pipelines and oversees the technical aspects of data operations
• Ensure data processes are optimized and align with business requirements
• Identify areas for process improvements and suggests tools and technologies to enhance efficiency
• Continuously improve data infrastructure automation, ensuring reliable and efficient data processing
• Lead the development and maintenance of data pipelines and ETL processes using technologies such as Dagster and DBT to ensure efficient data flow and processing
• Automate data ingestion, transformation, and loading processes to support blockchain data analytics and reporting
• Utilize Snowflake data warehousing solutions to manage and optimize data storage and retrieval
• Collaborate with Engineering Leadership and Product teams to articulate data strategies and progress
• Promote best practices in data engineering, cloud infrastructure, networking, and security