Data Analyst

This onsite Data Analyst role supports a quantitative trading environment by delivering reliable, scalable data pipelines and automation for data quality. Responsibilities Develop and maintain batch and real-time data pipelines using Python and SQL. Ingest, cleanse, validate, and normalize structured and unstructured datasets from multiple sources. Build and improve data quality controls, validation frameworks, reconciliation processes, and anomaly detection solutions. Work with large financial and alternative datasets to maintain accuracy, consistency, and reliability. Support new data provider onboarding 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 to automate data processing, classification, extraction, and enrichment workflows. Evaluate and help implement LLM-based solutions for data mapping, documentation, and quality assurance. Monitor production workflows and investigate data issues, outliers, and operational anomalies. Create documentation and data dictionaries to support long‑term scalability and process improvements. Collaborate on cloud‑based data platform initiatives and modern data architecture projects. Own datasets and processes, identifying opportunities for efficiency and automation. Requirements Bachelor's, Master's, or PhD in Computer Science, Engineering, Mathematics, Statistics, Economics, or a related quantitative discipline. Strong development skills in Python and SQL. 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 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 to explain technical concepts to technical and non‑technical audiences. Strong analytical thinking, attention to detail, and problem‑solving ability. Location and Compensation Location: New York, NY (onsite) Salary: USD 150,000 - 180,000 per year Technologies Python, SQL Airflow, Dagster

AWS, GCP

Snowflake, BigQuery, Databricks Machine Learning, NLP, LLM-based solutions Git, Linux Preferred Background 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. #J-18808-Ljbffr Selby Jennings