Senior Quantitative Data Engineer

YipitData is the leading market research and analytics firm for the disruptive economy, seeking a Senior Quantitative Data Engineer to join their Data Engineering team. The role involves designing and building data pipelines for quantitative investment firms, ensuring data integrity and compliance while collaborating with various teams to meet client needs.

Responsibilities:

• Design, build, and operate scalable, efficient data pipelines that integrate and standardize internal and third-party alternative/financial data into analysis-ready formats to support systematic investment research
• Partner with Quant Research, Data Infrastructure, Product, and Revenue to align pipelines, model/data requirements, and client SLAs
• Architect PIT-compliant, look-ahead, and leakage-free datasets for quant research/backtesting. Implement PIT-aware “as-of” version backfills and robust handling of late-arriving data. Build data integrity checks for time-series/panel datasets, including de-duplication and outlier/anomaly detection
• Develop robust data validation and monitoring systems to ensure accuracy, timeliness, and reproducibility of all delivered datasets
• Implement and optimize data feeds for external delivery to quant clients (APIs, S3, real-time streaming)
• Contribute to product discovery and R&D, helping define the data architecture and infrastructure strategy for the Quant initiative
• Ensure compliance with and adherence to governance best practices (versioning, documentation, access controls)

Requirements:

• 6+ years of experience as a Data Engineer or Quantitative Data Engineer at a financial firm, data provider, or technology company
• Strong communicator with experience working with both internal and external stakeholders
• Proven track record building and maintaining large-scale ETL pipelines using Python and distributed data technologies (e.g., Spark, Airflow, Snowflake, Databricks)
• Experience working with financial, alternative, or time-series data used in quantitative investment workflows
• Strong understanding of data modeling, schema design, and metadata management
• Experience with data delivery systems such as S3 feeds, APIs, or data sharing platforms such as Snowflake Share or Delta Sharing
• Strong communication skills and a collaborative mindset, with the ability to translate between technical and research stakeholders
• A passion for data reliability, reproducibility, and performance
• Familiarity with cloud-based data infrastructure (AWS preferred)
• Deep curiosity about financial markets and a passion for data-driven investing