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Market Data Engineer Jobs (NOW HIRING)

AI Data Engineer

New York, NY · On-site

$125K - $150K/yr

Develop ETL/ELT processes for diverse data sources including market data, research documents ... Partner with AI engineers, software developers, and data scientists to understand data requirements.

AI Data Engineer

New York, NY · On-site

$125K - $150K/yr

Develop ETL/ELT processes for diverse data sources including market data, research documents ... Partner with AI engineers, software developers, and data scientists to understand data requirements.

Data Engineer

San Francisco, CA · On-site

$134K - $162K/yr

... market data feeds or bank systems) and to feed data into external systems as needed. • Collaborate with backend engineers to ensure that the application's data storage and retrieval logic is ...

Data Engineer

$117K - $140K/yr

... market data, customer lifecycle events, marketplace results, and utility rate feeds into clean, reliable, and well-documented data assets. • Partner closely with engineering, operations, and ...

Sr. Data Engineer

Manhattan, NY · On-site

$126K - $151K/yr

The Senior Data Engineer will design and scale the data infrastructure supporting my clients ... market data vendors. We need someone who understands that distinction and can build for it.

Data Engineer

Manhattan, NY · On-site

$75 - $85/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Genesis10 is seeking a Data Engineer for a 6 month + contract position with an investment ... Financial market data literacy with product knowledge spanning equities, fixed income, futures, and ...

Data Engineer

$117K - $140K/yr

... market data, customer lifecycle events, marketplace results, and utility rate feeds into clean, reliable, and well-documented data assets. • Partner closely with engineering, operations, and ...

Data Engineer

$117K - $140K/yr

... market data, customer lifecycle events, marketplace results, and utility rate feeds into clean, reliable, and well-documented data assets. • Partner closely with engineering, operations, and ...

$107K - $142K/yr

Experience with Market Data * Knowledge of microservice architectures * Knowledge of clean code principles * DevOps mindset is an asset * Cloud Networking experience (BGP, VPC peering) * Experience ...

Data Engineer

$117K - $140K/yr

... market data, customer lifecycle events, marketplace results, and utility rate feeds into clean, reliable, and well-documented data assets. • Partner closely with engineering, operations, and ...

Data Engineer

Miami, FL · On-site

$109K - $131K/yr

Data Engineer ONSITE - Miami, FL 6+ Months Contract-to-Hire * Strong data experience is required ... market data context. • Contribution to open-source data projects or dbt packages Skills:

Showing results 41-60

Market Data Engineer information

See salary details

$44.5K

$129.7K

$177.5K

How much do market data engineer jobs pay per year?

As of Aug 17, 2026, the average yearly pay for market data engineer in the United States is $129,716.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,500.00 and $137,500.00 per year, depending on experience, location, and employer.

What are some common challenges Market Data Engineers face when integrating new data sources?

Market Data Engineers often encounter challenges related to data quality, format inconsistencies, and latency when integrating new data sources. Adapting to different vendor APIs, ensuring real-time data streaming, and maintaining data integrity are crucial tasks that can be technically demanding. Collaborating closely with trading, analytics, and IT teams is essential to troubleshoot issues quickly and deliver reliable data pipelines. Staying current with evolving data protocols and regulatory requirements also plays a significant role in the daily responsibilities of a Market Data Engineer.

What are the key skills and qualifications needed to thrive as a Market Data Engineer, and why are they important?

To thrive as a Market Data Engineer, you need strong programming skills (such as Python, Java, or C++), a deep understanding of financial markets, and a relevant degree in computer science or a related field. Familiarity with market data feeds (like Bloomberg or Reuters), FIX protocol, and real-time data processing systems is typically required. Analytical thinking, attention to detail, and effective communication are essential soft skills for excelling in this role. These skills and qualities are crucial for ensuring accurate, reliable, and efficient delivery of market data to support trading and financial decision-making.

What is the difference between Market Data Engineer vs Data Analyst?

AspectMarket Data EngineerData Analyst
Required CredentialsBachelor's in Computer Science, Data Science, or related field; experience with data engineering toolsBachelor's in Statistics, Mathematics, or related field; proficiency in data analysis tools
Work EnvironmentFinancial firms, trading platforms, data providersBusiness, finance, marketing departments across industries
Employer & Industry UsageFinancial services, trading firms, market data providersVarious industries including finance, marketing, healthcare

While both roles handle data, Market Data Engineers focus on building and maintaining data infrastructure for market data, whereas Data Analysts interpret data to generate insights. The roles often overlap in data handling but differ in technical depth and focus.

What does a market data engineer do?

A market data engineer designs, develops, and maintains systems for collecting, processing, and distributing financial market data. They work with large datasets, use programming languages like Python or Java, and often utilize database and data pipeline tools to ensure accurate and timely data delivery for trading and analysis purposes.

What cities are hiring for Market Data Engineer jobs?

Cities with the most Market Data Engineer job openings:

What states have the most Market Data Engineer jobs?

States with the most job openings for Market Data Engineer jobs include:

Infographic showing various Market Data Engineer job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, and 4% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $129,716 per year, or $62.4 per hour.

AI Data Engineer

Schonfeld

New York, NY • On-site

$125K - $150K/yr

Full-time

Re-posted 12 days ago


Job description

About the Role

Schonfeld Strategic Advisors is seeking an experienced AI Data Engineer to join our Data Engineering team. In this role, you will be responsible for designing, building, and maintaining robust data pipelines that power SchonAI, our firm's internal AI platform. You will work at the intersection of data engineering and AI, ensuring that high-quality, timely, and relevant data flows seamlessly to our AI systems to support investment professionals across the firm.

Key Responsibilities

Data Pipeline Development

  • Design and build scalable, reliable data pipelines to ingest, transform, and deliver structured and unstructured data to SchonAI using Prefect.
  • Develop ETL/ELT processes for diverse data sources including market data, research documents, internal databases, and third-party APIs.
  • Implement real-time and batch data processing workflows to meet varying latency requirements.
  • Ensure data quality, consistency, and integrity across all pipelines.

AI Data Infrastructure

  • Build and maintain data infrastructure optimized for AI/ML workloads, including vector databases and semantic search systems.
  • Design data schemas and storage solutions that support efficient retrieval and processing for LLM applications.
  • Implement data versioning, lineage tracking, and observability for AI training and inference pipelines.
  • Optimize data delivery for low-latency AI interactions and high-throughput batch processing.

Integration & Collaboration

  • Partner with AI engineers, software developers, and data scientists to understand data requirements.
  • Integrate with existing firm systems including risk platforms, trading systems, portfolio management tools, and research databases.
  • Collaborate with infrastructure teams on cloud architecture, security, and compliance requirements.
  • Work closely with business stakeholders to prioritize data sources and pipeline enhancements.

Data Governance & Security

  • Implement appropriate data access controls, encryption, and compliance measures.
  • Ensure adherence to data governance policies and regulatory requirements.
  • Monitor and maintain data pipeline performance, reliability, and cost efficiency.
  • Document data flows, transformations, and dependencies.

Required Qualifications

Technical Skills

  • Programming: Strong proficiency in Python; experience with SQL and at least one other language (e.g. Java, Scala, Go, Rust)
  • Data Engineering: 5+ years of experience building production data pipelines using tools like Apache Airflow, Prefect, Dagster, or similar
  • Big Data Technologies: Hands-on experience with distributed computing frameworks (Spark, Flink) and modern data platforms
  • Cloud Platforms: Proficiency with AWS services (S3, Kubernetes) or equivalent GCP services
  • Databases: Experience with both SQL (PostgreSQL, MySQL) and NoSQL databases (MongoDB, DynamoDB, Elasticsearch)
  • AI/ML Data: Understanding of data requirements for ML/AI systems, including experience with vector databases (Pinecone, Weaviate, Qdrant) and embedding pipelines

Preferred Experience

  • Experience building data pipelines for LLM applications or RAG (Retrieval Augmented Generation) systems
  • Familiarity with financial data sources (market data, fundamental data, alternative data)
  • Knowledge of data streaming technologies (Kafka, Kinesis, Pub/Sub)
  • Experience of Analytics/Warehouse/OLAP DB (BigQ, SingleStore, RedShift, ClickHouse)
  • Experience with containerization (Docker) and orchestration (Kubernetes)
  • Understanding of MLOps practices and tools
  • Experience with data quality frameworks (Great Expectations, Deequ)

 Professional Skills

  • Bachelor's or Master's degree in Computer Science, Data Engineering, or related technical field
  • Strong problem-solving skills and attention to detail
  • Excellent communication skills with ability to translate technical concepts for non-technical stakeholders
  • Experience working in fast-paced, collaborative environments
  • Self-motivated with ability to manage multiple priorities

Who we are  
Schonfeld is a global multi-manager hedge fund that strives to deliver industry-leading risk-adjusted returns for our investors. We leverage both internal and external portfolio manager teams around the world, seeking to capitalize on inefficiencies and opportunities within the markets. We draw from decades of experience and a significant investment in proprietary technology, infrastructure and risk analytics to invest across four main strategies: Quant, Tactical, Fundamental Equity and Discretionary Macro & Fixed Income.

Our Culture
At Schonfeld, we'll invest in you. Attracting and retaining top talent is at the heart of what we do, because we believe that exceptional outcomes begin with exceptional people. We foster a culture where talent is empowered to continually learn, innovate and pursue ambitious goals. We are teamwork-oriented, collaborative and encourage ideas-at all levels-to be shared. As an organization committed to investing in our people, we provide learning and educational offerings and opportunities to make an impact. We encourage community through internal networks, external partnerships and service initiatives that promote inclusion and purpose beyond the firm's walls.

The base pay for this role is expected to be between $225k and $275k. The expected base pay range is based on information at the time this post was generated. This role may also be eligible for other forms of compensation such as a performance bonus and a competitive benefits package. Actual compensation for the successful candidate will be determined based on a variety of factors such as skills, qualifications, and experience.

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