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Senior Data Engineer Remote

  • NEW YORK, NY, United States

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Original job description

Genesis10 is currently seeking a Senior Data Engineer for a remote position with an investment management firm located in New York, NY. This is a 6+ month contract opportunity.We are looking for an experienced Senior Data Engineer who excels at building and refining robust data systems. 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 like Snowflake and DBT. You'll work closely with both technical and non-technical teams, ensuring that complex solutions are clearly communicated and effectively implemented. If you value a balanced approach that combines thoughtful innovation with high-quality execution, this opportunity offers the chance to play a key role in strengthening our data infrastructure while contributing to our broader mission. 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 Desired 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

More about this job

Skills

  • Risk management
  • Snowflake
  • Data engineering
  • Python
  • Computer science
  • Collaboration
  • SQL
  • Process data
  • Data modelling
  • Microsoft SQL Server
  • Docker
  • Kubernetes
  • Data warehousing
  • Apache Spark
  • Apache Kafka
  • Problem solving
  • Databricks
  • AWS
  • EMR

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