Data Analyst

Selby Jennings; New York (NY); On-site; USD 150,000 - 180,000; Full time

Job summary

Selby Jennings is seeking an onsite Data Analyst in New York to develop and maintain batch and real-time data pipelines using Python and SQL. You will ingest, cleanse, validate, and normalize diverse datasets while building data quality controls and anomaly detection.

You will work with large financial datasets, onboard new data providers, and collaborate with engineering and business teams to improve data accessibility. Experience with ML/NLP and LLM-driven workflows is a plus.

Qualifications

• 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 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.

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.
• Work with large financial and alternative datasets to maintain accuracy, consistency, and reliability.
• Support new data provider onboarding by reviewing specs, mapping fields, and integrating APIs.
• Partner with engineering and business teams to define data requirements and improve accessibility.
• Apply machine learning and AI to automate data processing, classification, extraction, and enrichment.
• Evaluate and help implement LLM-based solutions for data mapping, documentation, and QA.
• Monitor production workflows and investigate data issues, outliers, and operational anomalies.
• Create documentation and data dictionaries to support 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.

Skills

Python

SQL

Data pipelines

Data quality

Machine learning

NLP

Data analysis

Education

Bachelor's/Master's/PhD in CS/Engineering/Math/Stats/Economics

Tools

Airflow

Dagster

AWS

GCP

Snowflake

BigQuery

Databricks

Git

Linux

Job description

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.