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Ice Data Services Jobs (NOW HIRING)

MIO Partners

Strategy Lead, Market Data

New York, NY · On-site

Manage relationships with key data providers, including Bloomberg, LSEG, ICE Data Services, and others * Support contract negotiations, renewals, and cost optimization efforts * Monitor data usage ...

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Ice Data Services information

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$17.5K

$83.6K

$188K

How much do ice data services jobs pay per year?

As of Jul 23, 2026, the average yearly pay for ice data services in the United States is $83,595.00, according to ZipRecruiter salary data. Most workers in this role earn between $34,500.00 and $124,500.00 per year, depending on experience, location, and employer.

Is ICE a good company?

ICE Data Services is a division of Intercontinental Exchange that provides financial data, analytics, and trading technology. It is generally regarded as a reputable company within the financial industry, offering roles that require strong analytical skills and familiarity with financial markets. Employee reviews and industry reports can provide additional insights into its work environment and culture.

What jobs in the US pay 300,000 a year?

In the finance and data services sectors, senior roles such as quantitative analysts, data scientists, and senior software engineers can earn $300,000 or more annually, especially with experience, advanced skills, and relevant certifications. Executive positions like chief data officers or vice presidents may also reach or exceed this salary level, often requiring leadership experience and specialized knowledge of financial markets or data infrastructure.

How does working at ICE Data Services typically involve collaboration with other teams or departments?

At ICE Data Services, professionals often collaborate closely with teams such as technology, product development, sales, and client support to deliver accurate and timely market data solutions. This cross-functional teamwork is essential for ensuring that data products meet client needs and regulatory requirements. Employees regularly participate in project meetings, product launches, and troubleshooting sessions, where clear communication and a willingness to share expertise are highly valued. This collaborative environment not only enhances service quality but also provides opportunities to learn from colleagues across different disciplines.

What is the difference between Ice Data Services vs Data Analyst?

AspectIce Data ServicesData Analyst
Required CredentialsBachelor's degree, industry certifications (e.g., CFA, FRM)Bachelor's or master's in data science, statistics, or related fields
Work EnvironmentFinancial services, data providers, trading platformsVarious industries including finance, marketing, healthcare
Employer & Industry UsageFinancial institutions, commodity markets, data vendorsCorporations, consulting firms, government agencies
Common Search & Comparison IntentUnderstanding roles in financial data servicesCareer options, job responsibilities, skills required

Ice Data Services primarily provides financial data and analytics to support trading and investment decisions within the financial industry. Data Analysts interpret data to inform business strategies across various sectors. While both roles involve working with data, Ice Data Services focuses on data provision and financial markets, whereas Data Analysts focus on analyzing data to generate insights for diverse industries.

What is the salary of data analyst in ICE?

The salary of a data analyst at ICE (Intercontinental Exchange) typically ranges from $60,000 to $90,000 annually, depending on experience, location, and specific role requirements. Entry-level positions may start lower, while experienced analysts with specialized skills or certifications can earn higher salaries. Compensation often includes benefits such as health insurance and retirement plans.

What is Ice Data Services?

ICE Data Services is a division of Intercontinental Exchange (ICE) that provides comprehensive financial market data, analytics, and related services to financial institutions, traders, and investors worldwide. Their offerings include real-time and historical data on equities, fixed income, commodities, derivatives, and foreign exchange markets. ICE Data Services also delivers pricing, valuation, reference data, and risk management tools, helping clients make informed investment and trading decisions. The service is widely used by banks, asset managers, and other financial professionals to support compliance, research, and operational needs.

What do ICE data services do?

ICE Data Services provides financial market data, analytics, and trading tools to support investment decisions and risk management. Roles in this area often involve data analysis, software proficiency, and understanding of financial markets. Employees may work with large datasets, programming languages, and industry regulations.

What are the key skills and qualifications needed to thrive as an Ice Data Services professional, and why are they important?

To thrive at Ice Data Services, you need strong analytical skills, a solid background in finance or data science, and typically a relevant degree such as finance, economics, or computer science. Familiarity with financial data platforms, market data feeds, and proficiency in tools like Excel, SQL, or Python are commonly required, along with knowledge of industry-specific certifications such as CFA or FRM. Excellent attention to detail, communication skills, and the ability to manage multiple tasks make professionals stand out in this field. These skills ensure accurate data delivery, effective client solutions, and high performance in the fast-paced financial information industry.
More about Ice Data Services jobs
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Ice Data Services jobs near you
Infographic showing various Ice Data Services job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, 1% Temporary, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $83,595 per year, or $40.2 per hour.
Senior Data Engineer

Senior Data Engineer

Intercontinental Exchange Holdings, Inc.

Atlanta, GA • On-site

$101K - $138K/yr

Full-time

Posted 9 days ago

Job description

Overview
Job Purpose
ICE Data Services (an Intercontinental Exchange company) is seeking a Senior Data Engineer to join its Data Impact & Innovation team. This team supports a variety of reference data, index, climate finance, and alternative data products. The role contributes to the data platforms and pipelines that help the financial sector understand and respond to carbon transition risk, physical risk, and related challenges.
Our team maintains a global-scale geospatial data platform in Google BigQuery, holding many terabytes of data across carbon transition risk, physical climate risk, and social/demographic features - feeding analytical products for fixed income and real estate financial instruments, supporting the ambitious product roadmap for ICE Climate and other data products. Our engineering stack includes:
  • Orchestration: Airflow, moving toward composable task abstractions over a shared pipeline framework
  • Transformation: dbt, and other data lineage and DQA tools, primarily using Google BigQuery
  • Geospatial processing: Python (GeoPandas, Shapely, GeoAlchemy2 against PostGIS) for vector operations, and R
  • Execution and compute environments: Hybrid across Google Cloud Platform and on-premise RHEL Linux infrastructure
  • Ingestion: Third-party vendor feeds via API, SFTP, cloud storage, and database replication

Typical engineering challenges include working with data science and climate science teams in operationalizing trained models and data pipelines, absorbing upstream vendor corrections and historical restatements without corrupting downstream artifacts, scaling raster x vector joins at terabyte scale, evolving schemas and spatial-indexing strategies as data sources broaden, and balancing long-running batch workflows against emerging sub-daily refresh cadences.
Responsibilities
  • Take significant components of the data platform from "works" to "mature" - tightening reliability, observability, cost/performance characteristics, and operational discipline across our ingestion, transformation, and serving layers.
  • Establish and foster adoption of technical standards for the team's work - including Airflow DAG structure, dbt model layout, BigQuery schema and partitioning conventions, pipeline testing practices, and deployment workflows.
  • Lead technical design discussions, mentor other data engineers through code review, pairing, and design-doc review, and grow them along their career path.
  • Act as a technical point of contact for cross-functional initiatives - partnering with data science, climate science, product, and infrastructure colleagues to drive forward decisions and make tradeoffs explicit.
  • Deliver day-to-day work across the stack above - authoring Airflow DAGs and dbt models, contributing geospatial processing capabilities, and shipping cleanly partitioned, audit-friendly outputs from ingestion through serving.
  • Support data science and climate science teams by helping design the tooling, training, and validation environments, and by deploying their trained models into production.
  • Effectively leverage AI and LLM-based developer tooling to accelerate development workflows and improve code quality.
  • Identify opportunities to improve and optimize data pipelines - for speed, cost, robustness, integrity, and operational simplicity.
  • Work with business analysts, product management, and adjacent engineering teams to understand and refine new data requirements.

Knowledge and Experience
  • 5+ years of professional experience as a data engineer, with a track record of architecting, shipping, and operating production data pipelines end-to-end.
  • Experience mentoring and developing other data engineers - through code review, pairing, design discussions, and career coaching.
  • Ability to establish and foster adoption of technical standards.
  • A habit of actively monitoring, evaluating, and prototyping emerging big-data, geospatial, and machine-learning technologies and platforms - staying conversant in advances across cloud data engines, geospatial libraries and standards, and ML/MLOps frameworks - and bringing the most promising into the team's design discussions, evaluations, and adoption decisions.
  • Strong system-design judgment across the tradeoff space of performance, cost, maintainability, and auditability
  • Comfort scoping, decomposing, and delegating work for other engineers.
  • Strong written and verbal communication - able to translate technical tradeoffs for senior business, product, and client stakeholders.
  • Deep fluency in modern, typed Python as a primary working language, including comfort with type-driven design (e.g. Pydantic v2).
  • Strong SQL background, including experience partitioning, clustering, and performance-tuning queries on modern cloud warehouses - Google BigQuery experience strongly preferred.
  • Production experience with dbt for managing warehouse transformations, and with Airflow (or a comparable orchestrator) for workflow orchestration.
  • Solid grounding in geospatial data engineering - Python tooling (GeoPandas, Shapely), spatial databases (PostGIS), raster processing, or adjacent skills.
  • A systems-thinking orientation: anticipates cascading effects of upstream data changes, schema evolution, and vendor corrections; designs pipelines with observability, auditability, and graceful failure in mind.
  • Comfort owning production incidents and debugging distributed systems.
  • Experience working cooperatively with systems, network, and infrastructure engineering and operations teams to ensure proper monitoring, alerting, and incident response workflows.
  • Demonstrated ability to integrate AI/LLM coding assistants productively - treating them as a force multiplier rather than a substitute for judgment.
  • Curiosity about the financial and climate/geospatial domains and contexts the team operates in.

Preferred Knowledge and Experience
  • Well-versed in and opinionated about the modern Python ecosystem.
  • Exposure to columnar and lakehouse technologies (Parquet, ClickHouse, DuckDB).
  • Working understanding of data lineage, data quality validation, and metadata/cataloging frameworks.
  • Prior experience in a hybrid cloud + on-premise environment, and with full software development lifecycle (SDLC) best practices and processes.
  • Prior exposure to ML deployment workflows - supporting data science teams with training tooling and/or model-serving infrastructure.
  • Familiarity with R, particularly geospatial packages.

#LI-HR1 #LI-ONSITE
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Intercontinental Exchange, Inc. is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to legally protected characteristics.

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