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Data Analyst

Manhattan, US • Posted 1 day ago • Updated 1 day ago

Full Time

On-site

Fitment

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Job Details

Skills

Research
Innovation
Real-time
Onboarding
Mapping
Accessibility
Data Processing
Extraction
Data Mapping
Quality Assurance
Documentation
Process Improvement
Scalability
Collaboration
Data Architecture
Computer Science
Mathematics
Statistics
Economics
Python
SQL
Data Engineering
Quantitative Research
Data Modeling
Extract
Transform
Load
Orchestration
Cloud Computing
Amazon Web Services
Google Cloud Platform
Google Cloud
Data Warehouse
Snow Flake Schema
Databricks
Machine Learning (ML)
Natural Language Processing
Data Quality
Git
Linux
Testing
Software Engineering
Communication
Analytical Skill
Attention To Detail
Conflict Resolution
Problem Solving
Market Analysis
Trading
Bloomberg
Finance
Analytics
Artificial Intelligence
Workflow

Research
Innovation
Real-time
Onboarding
Mapping
Accessibility
Data Processing
Extraction
Data Mapping
Quality Assurance
Documentation
Process Improvement
Scalability
Collaboration
Data Architecture
Computer Science
Mathematics
Statistics
Economics
Python
SQL
Data Engineering
Quantitative Research
Data Modeling
Extract
Transform
Load
Orchestration
Cloud Computing
Amazon Web Services
Google Cloud Platform
Google Cloud
Data Warehouse
Snow Flake Schema
Databricks
Machine Learning (ML)
Natural Language Processing
Data Quality
Git
Linux
Testing
Software Engineering
Communication
Analytical Skill
Attention To Detail
Conflict Resolution
Problem Solving
Market Analysis
Trading
Bloomberg
Finance
Analytics
Artificial Intelligence
Workflow

Summary

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 Google Cloud Platform.
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, 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.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 90922487
Position Id: 24864422
Posted 1 day ago

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