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Reference Data Management Jobs in New York (NOW HIRING)

TiltEdge Solutions LLC

Data Engineer

Jersey City, NJ · On-site

$75/hr

Support Master Data Management (MDM) initiatives across security, account, client, and reference data domains. * Collaborate with data architects, business analysts, governance teams, and application ...

KBRA

Data Steward - Data Operations (NY)

New York, NY · Hybrid

$75K - $90K/yr

Experience working with fixed-income products, reference data, entity management, or ratings operations preferred. * Analytical and problem-solving skills with exceptional attention to detail.

KBRA

Data Steward - Data Operations (NY)

New York, NY · On-site

$75K - $90K/yr

Experience working with fixed-income products, reference data, entity management, or ratings operations preferred. * Analytical and problem-solving skills with exceptional attention to detail.

Munich RE

Data Governance Analyst

Manhattan, NY · On-site

$114K/yr

Manage both existing and to be defined reference data sets through the further development of procedures, routines, approval flows and processes ensuring consistency, comprehensiveness, and ...

Compunnel

SIMCORP GAIN Consultant

New York, NY · On-site

The role focuses on managing inbound market data, reference data, and security master files using the SimCorp GAIN platform, formerly known as AIM Gain. The consultant will support data quality ...

Nastech Global

Data Modeler

New York, NY · On-site

$60 - $77.75/hr

Data management background with hands-on work experience in supporting teams that are building and ... Experience in the financial sector with Market securities reference data and fixed income such as ...

AuxoAI Inc.

MDM Data Specialist

Manhattan, NY · On-site

$100 - $130/hr

Configure and support MDM platform capabilities, including datamodels, match and merge rules, survivorship rules, hierarchy management,validation rules, workflows, reference data, and data quality ...

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All JobsReference Data Management JobsReference Data Management Jobs in New York

Reference Data Management information

See New York salary details

$33.9K

$106.3K

$188.2K

How much do reference data management jobs pay per year?

As of Sep 3, 2026, the average yearly pay for reference data management in New York is $106,280.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,200.00 and $137,300.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a reference data management professional?

To thrive in Reference Data Management, you need strong analytical skills, attention to detail, and a background in information systems or business, often with a relevant degree. Familiarity with data management tools such as SQL, data governance platforms, and master data management (MDM) systems, as well as knowledge of industry data standards, is typically required. Excellent communication, problem-solving abilities, and stakeholder management are essential soft skills for ensuring data quality and alignment across departments. These skills are crucial for maintaining accurate, consistent reference data that supports effective business operations and regulatory compliance.

What are some common challenges faced by professionals in reference data management roles, and how can they be addressed?

Professionals in Reference Data Management often encounter challenges related to maintaining data accuracy, consistency, and completeness across multiple systems. Dealing with legacy systems, data silos, and frequent data updates can make it difficult to ensure data quality. To address these issues, it's essential to implement robust data governance policies, leverage automated data validation tools, and collaborate closely with IT and business teams. Regular training and clear communication channels also help in keeping data standards aligned across the organization.

What is the difference between Reference Data Management vs Data Analyst?

AspectReference Data ManagementData Analyst
Primary FocusManaging and maintaining reference data across systemsAnalyzing data to generate insights and reports
Skills & CertificationsData governance, data quality, database managementStatistical analysis, data visualization, SQL, Excel
Work EnvironmentIT departments, data management teamsBusiness units, analytics teams
Industry UsageFinancial services, healthcare, retailMarketing, finance, operations

While Reference Data Management focuses on maintaining consistent reference data across systems, Data Analysts interpret data to support decision-making. Both roles require strong data skills but serve different functions within organizations.

What is reference data management?

Reference data management is the process of organizing, maintaining, and governing key data elements that are used across multiple systems and processes within an organization. It ensures data consistency, accuracy, and standardization, often involving tools like data governance platforms and data quality software. Professionals in this field need strong attention to detail and knowledge of data standards and compliance requirements.

What are popular job titles related to Reference Data Management jobs in New York?

For Reference Data Management jobs in New York, the most frequently searched job titles are:

What job categories do people searching Reference Data Management jobs in New York look for?

The top searched job categories for Reference Data Management jobs in New York are:

Reference Data Management jobs near you
Infographic showing various Reference Data Management job openings in New York as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 15% Part Time, and 2% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution, with an average salary of $106,280 per year, or $51.1 per hour.

Data Engineer

TiltEdge Solutions LLC

Jersey City, NJ • On-site

$75/hr

Contractor

Re-posted 29 days ago

Job description

Job Title: Sr Data Engineer 
 
Location:  Jersey City, NJ(Hybrid). 
 
Duration: 6-12 Months
 
Rate: DOE
 
 
Job Description:
 
We are seeking a hands-on Data Engineer with strong experience in building scalable enterprise data solutions within Financial Services environments. The ideal candidate will have expertise in cloud-based data platforms, modern data engineering practices, and large-scale data integration initiatives supporting operational, analytical, and regulatory data needs.
This role requires strong technical capabilities in data pipeline development, cloud data processing, Master Data Management (MDM), and enterprise data integration. The candidate should be comfortable working across complex distributed environments and partnering with architecture, analytics, governance, and business teams to deliver reliable, secure, and scalable data solutions.
 
Key Responsibilities:
•                    Design, develop, and support scalable data pipelines and enterprise data integration solutions.
•                    Build and maintain batch and real-time data ingestion, transformation, and processing frameworks.
•                    Develop cloud-native data engineering solutions supporting enterprise data lake, warehouse, and lakehouse platforms.
•                    Implement ETL/ELT processes for structured, semi-structured, and unstructured data sources.
•                    Support Master Data Management (MDM) initiatives across security, account, client, and reference data domains.
•                    Collaborate with data architects, business analysts, governance teams, and application teams to support enterprise data initiatives.
•                    Implement data quality validation, monitoring, metadata management, and lineage processes.
•                    Support cloud migration and modernization efforts involving legacy and enterprise data platforms.
•                    Optimize data processing, storage, and pipeline performance for scalability and operational efficiency.
•                    Ensure compliance with enterprise security, governance, and regulatory standards within financial services environments.
•                    Support reporting, analytics, and downstream consumption platforms through reliable and trusted data delivery.
 
Required Skills & Experience:
•                    Strong hands-on experience in Data Engineering and enterprise-scale data integration.
•                    Proven experience developing scalable ETL/ELT pipelines and distributed data processing solutions.
•                    Experience working with modern cloud-based data platforms and data ecosystems.
•                    Hands-on expertise with Strong SQL expertise along with programming/scripting experience in Python, PySpark, or Snowpark.
•                    Experience with dbt (Data Build Tool) for:
·               Data transformation and modeling
·               ELT pipeline development within Snowflake/Databricks
·               Modular, reusable SQL-based data workflows
·               Data testing, documentation, and version control integration
•                    Experience with cloud platforms such as Azure, AWS, or GCP, including integration with Snowflake and Databricks.
•                    Solid understanding of data lake, data warehouse, and lakehouse architectures, and their implementation across platforms.
•                    Experience with orchestration and workflow tools (e.g., Airflow, Databricks Workflows, Snowflake Tasks) for pipeline scheduling and automation.
•                    Experience supporting Master Data Management (MDM) and enterprise data governance initiatives.
•                    Familiarity with metadata management, data lineage, data cataloging, and data quality processes.
•                    Experience integrating diverse data sources, including:
·               APIs and microservices
·               File-based ingestion (batch)
·               Real-time/streaming data (e.g., Kafka, Spark Streaming)
•                    Knowledge of performance tuning, cost optimization, and scalability techniques across both Spark-based and Snowflake environments.
•                    Understanding of enterprise security, compliance, and governance standards, including RBAC, data masking, and encryption.
•                    Experience working in Agile and DevOps environments, including CI/CD for data pipelines.
 
Preferred Qualifications:
•                    Financial Services or Banking industry experience preferred.
•                    Experience supporting regulatory, risk, compliance, or operational reporting data environments.
•                    Exposure to real-time data processing and streaming technologies.
•                    Familiarity with CI/CD processes and infrastructure automation.
•                    Strong analytical, troubleshooting, and problem-solving skills.
•                    Excellent communication and collaboration skills.
 
Education:
Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field.
 
Job Responsibilities
•                    Design, develop, and support scalable data pipelines and enterprise data integration solutions.
•                    Build and maintain batch and real-time data ingestion, transformation, and processing frameworks.
•                    Develop cloud-native data engineering solutions supporting enterprise data lake, warehouse, and lakehouse platforms.
•                    Implement ETL/ELT processes for structured, semi-structured, and unstructured data sources.
•                    Support Master Data Management (MDM) initiatives across security, account, client, and reference data domains.
•                    Collaborate with data architects, business analysts, governance teams, and application teams to support enterprise data initiatives.
•                    Implement data quality validation, monitoring, metadata management, and lineage processes.
•                    Support cloud migration and modernization efforts involving legacy and enterprise data platforms.
•                    Optimize data processing, storage, and pipeline performance for scalability and operational efficiency.
•                    Ensure compliance with enterprise security, governance, and regulatory standards within financial services environments.
•                    Support reporting, analytics, and downstream consumption platforms through reliable and trusted data delivery.