Key skills
PythonSQLETLELTData EngineeringData WarehousingAnalyticsBusiness IntelligenceSnowflakeDatabricksSparkApache SparkKafkaApache KafkadbtFastAPIAWSKubernetesDockerPostgresSQL ServerRisk ManagementCommunicationCollaboration
About this role
Genesis10 is seeking a Data Engineer for a 6 month + contract position with an investment management firm. In this role, you'll contribute to developing efficient data pipelines for trading and risk management while modernizing legacy systems with cloud-based tools.
Responsibilities:
- 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
- Ensure high data quality and observability using modern data governance tools
- Optimize large-scale data processing workflows for efficiency and performance
- Collaborate with technical and non-technical teams to understand data requirements and implement effective solutions
- Partner with product managers and business stakeholders to understand data needs and translate them into scalable, well-documented data products
- Support and troubleshoot data pipelines, APIs, and database performance issues
Requirements:
- 5+ years of development experience with 2+ 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