Overview

• Join a leading quantitative trading firm where data, technology, and research drive business performance.
• Work with large-scale financial, market, vendor, and alternative datasets that support critical business and research functions.
• Partner with engineers, data professionals, and business stakeholders to build reliable, scalable, and automated data solutions.
• Contribute to a fast-paced, highly collaborative environment that values innovation, ownership, and technical excellence.

• Develop and maintain batch and real-time data pipelines using Python and SQL.
• Ingest, cleanse, validate, and normalize structured and unstructured datasets from a variety of sources.
• Build and enhance data quality controls, validation frameworks, reconciliation processes, and anomaly detection solutions.
• Work with large financial and alternative datasets to ensure accuracy, consistency, and reliability.
• Support the onboarding of new data providers by reviewing specifications, mapping fields, and integrating APIs.
• Partner with engineering and business teams to define data requirements and improve data accessibility.
• Apply machine learning and AI techniques to automate data processing, classification, extraction, and enrichment workflows.
• Assist in evaluating and implementing LLM-based solutions for data mapping, documentation, and quality assurance.
• Monitor production workflows and investigate data issues, outliers, and operational anomalies.
• Create documentation, data dictionaries, and process improvements to support long-term scalability.
• Collaborate on cloud-based data platform initiatives and modern data architecture projects.
• Take ownership of datasets and processes while identifying opportunities for efficiency and automation.

• Bachelor's, Master's, or PhD in Computer Science, Engineering, Mathematics, Statistics, Economics, or a related quantitative discipline.
• Strong Python and SQL development skills.
• Experience working with data engineering, analytics, quantitative research, financial data, or large-scale datasets.
• Knowledge of ETL development, data modeling, and data pipeline design.
• Exposure to workflow orchestration tools such as Airflow or Dagster.
• Experience with cloud platforms such as AWS or GCP.
• Familiarity with modern data warehouses including Snowflake, BigQuery, or Databricks.
• Hands-on experience with machine learning, NLP, or LLM-based solutions.
• Strong understanding of data quality, validation, reconciliation, and monitoring practices.
• Experience working with messy, incomplete, or high-volume datasets.
• Familiarity with Git, Linux, testing, and software engineering best practices.
• Excellent communication skills with the ability to explain technical concepts to both technical and non-technical audiences.
• Strong analytical thinking, attention to detail, and problem-solving ability.

• Experience with financial markets, market data, trading systems, or quantitative environments.
• Exposure to Bloomberg, LSEG, S&P, or other financial data vendors.
• Experience building automated analytics, monitoring, or AI-enabled data workflows.
• Interest in working in a high-performance, technology-driven environment.