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YipitData · 1 week ago

Senior Quantitative Data Engineer

United States

Full-time

Remote

Senior Level

$250K/yr - $250K/yr

6+ years exp

YipitData is the leading market research and analytics firm for the disruptive economy, providing insights across various sectors. They are seeking a Senior Quantitative Data Engineer to design and build scalable data pipelines for quantitative investment firms, ensuring data integrity and compliance while collaborating with multiple teams.

AnalyticsData VisualizationMarket Research

Growth Opportunities

No H1B

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)

Qualification

ETL pipelinesPythonData modelingCloud-based infrastructureFinancial data experienceData delivery systemsCommunicatorPassion for dataCollaborative mindset

Required

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