Senior Data Engineer - Remote

Genesis10 is currently seeking a Senior Data Engineer for a remote position with an investment management firm located in New York, NY. In this role, you'll contribute to a range of projects, from developing efficient data pipelines for trading and risk management to modernizing legacy systems with cloud-based tools.

Responsibilities:

• Evaluate and review data platform architecture, ensuring solutions are scalable, efficient, maintainable, and aligned with enterprise standards
• Assess the quality of technical designs and code produced by Mid-Level Data Engineers, providing guidance and recommendations to improve overall solution quality
• Develop and review detailed data models, table schemas, data structures, and database design artifacts to support enterprise data initiatives
• Serve as a technical mentor for mid-level Data Engineers, conducting design reviews, providing best-practice guidance, and supporting professional development
• Lead hands-on development activities across data engineering projects, contributing approximately 80% of time to technical implementation and 20% to design, architecture, and mentoring responsibilities
• Collaborate with architects and stakeholders to ensure data solutions meet performance, security, governance, and business requirements
• Identify architectural risks, performance bottlenecks, and optimization opportunities within existing and proposed data platforms
• Design, develop, and optimize data pipelines for trading, alpha generation, research, risk management, accounting, and more
• Build new golden source datasets such as security master, account master, and price master which are critical to the firm
• Develop shared Python libraries for data APIs, logging, and other core functionalities

Requirements:

• 8+ years of development experience with 3+ years focused on data engineering
• Bachelor's degree in computer science or related field
• Great communication skills and capability for cross-functional collaboration
• Excellent Python and SQL skills for data processing and automation
• Extensive ETL/ELT pipeline experience and expertise
• Strong understanding of data structures, data modeling, efficient query design, and performance tuning in a SQL database such as Postgres or MS SQL Server
• DBT for data transformation
• Experience building and deploying containerized applications (Docker, Kubernetes) in cloud environments
• Hands-on, production-level experience with Snowflake for cloud data warehousing — this is a core, non-negotiable requirement. Comfort working across multiple warehouse and lakehouse tools is a strong plus
• Proficiency with Apache Spark for large-scale and streaming distributed data processing
• Experience with Apache Kafka for streaming data ingestion and pipeline development
• Excellent problem-solving skills
• Experience with Databricks for unified analytics and data engineering workflows
• Hands-on experience with Apache Spark on AWS EMR for streaming and large-scale data processing use cases
• Experience with Apache Iceberg for open table format and large-scale, mutable dataset management in a lakehouse architecture
• Familiarity with data quality and observability tooling (DataFold, Great Expectations, DBT tests) and data catalog/governance platforms (OpenMetadata), including lineage integration
• Financial market data literacy with product knowledge spanning equities, fixed income, futures, and options
• Experience designing dashboards in a Business Intelligence tool
• Skill with a Python web API framework such as FastAPI