Data Analyst at Selby Jennings
New York, NY — full-time
Data Analyst | Quantitative Trading Firm 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. Responsibilities 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. Qualifications 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. Preferred Background Experience with financial markets, market data, trading systems,