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Data Reconciliation Analyst Jobs (NOW HIRING)

gpec

Credit/Reconciliation Analyst

Raleigh, NC

Account Invoicing and Reconciliation: * Input invoice information into appropriate billing portal ... Extract data, analyze, present and report trends from systems. Customer Relations: * Build ...

gpec

Credit/Reconciliation Analyst

Raleigh, NC · On-site

Account Invoicing and Reconciliation: * Input invoice information into appropriate billing portal ... Extract data, analyze, present and report trends from systems. Customer Relations: * Build ...

gpec

Credit/Reconciliation Analyst

Raleigh, NC · On-site

Account Invoicing and Reconciliation: * Input invoice information into appropriate billing portal ... Extract data, analyze, present and report trends from systems. Customer Relations: * Build ...

BCForward

Data Analyst

New York, NY · Remote

$30 - $42/hr

Data Analyst Location: Remote Contract Role Job Summary We are seeking a detail-oriented Data ... This role involves collecting, validating, reconciling, and maintaining workforce data across ...

CapLeo Global

Data Governance Analyst

Richmond, VA · On-site

Data Governance Analyst Job Location: Richmon VA Duration: 6 months C-H Hybrid About the Role Key ... reconciliation and validation checks across various systems • Come up with initial proof of ...

Global Pharma Tek

Data Governance Analyst

Richmond, VA · On-site

Data Governance Analyst Job Location: Richmon VA Duration: 6 months C-H Hybrid About the Role Key ... reconciliation and validation checks across various systems • Come up with initial proof of ...

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How much do data reconciliation analyst jobs pay per hour?

As of Jun 30, 2026, the average hourly pay for data reconciliation analyst in the United States is $31.19, according to ZipRecruiter salary data. Most workers in this role earn between $28.12 and $34.38 per hour, depending on experience, location, and employer.

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

To thrive as a Data Reconciliation Analyst, you need strong analytical skills, attention to detail, and a background in finance, accounting, or data management, often supported by a relevant degree. Familiarity with reconciliation software, advanced Excel functions, and enterprise resource planning (ERP) systems such as SAP or Oracle is typically required. Excellent problem-solving abilities, communication skills, and the capacity to work independently make candidates stand out in this role. These competencies ensure accurate data matching, timely identification of discrepancies, and support for efficient financial operations.

What are some typical challenges Data Reconciliation Analysts face when working with large and diverse data sources?

Data Reconciliation Analysts often encounter challenges related to integrating data from multiple systems that may use different formats, standards, or levels of data quality. Ensuring data accuracy and consistency across these sources requires keen attention to detail and strong analytical skills. Additionally, analysts may face tight deadlines and the need to troubleshoot discrepancies quickly, all while collaborating closely with IT, finance, and operations teams to resolve issues efficiently. Proficiency in reconciliation tools and clear communication with stakeholders are key to overcoming these challenges.

What are Data Reconciliation Analysts?

Data Reconciliation Analysts are professionals responsible for comparing and verifying data from different sources to ensure accuracy and consistency. Their main duties include identifying discrepancies, investigating the causes of mismatched records, and resolving errors to maintain data integrity. They often work with large datasets in industries such as finance, banking, or logistics, using specialized software tools to automate and streamline the reconciliation process. Strong analytical skills and attention to detail are essential in this role.

What is the difference between Data Reconciliation Analyst vs Data Analyst?

AspectData Reconciliation AnalystData Analyst
Required CredentialsBachelor's in Finance, Accounting, or related field; certifications like CPA or data analysis certificationsBachelor's in Statistics, Mathematics, or related field; certifications like Microsoft Excel or data analysis tools
Work EnvironmentFinancial institutions, accounting firms, or corporate finance departmentsVarious industries including marketing, healthcare, finance, and technology
Employer & Industry UsageUsed in finance, banking, and accounting sectors for data accuracyUsed across multiple industries for data insights and reporting

The Data Reconciliation Analyst primarily focuses on verifying and matching financial or transactional data to ensure accuracy, often within finance or accounting settings. In contrast, a Data Analyst interprets broader data sets to generate insights across various industries. While both roles require strong analytical skills, the Data Reconciliation Analyst emphasizes data accuracy and validation, whereas the Data Analyst emphasizes data interpretation and reporting.

More about Data Reconciliation Analyst jobs
What job categories do people searching Data Reconciliation Analyst jobs look for? The top searched job categories for Data Reconciliation Analyst jobs are:
Data Reconciliation Analyst jobs near you
Infographic showing various Data Reconciliation Analyst job openings in the United States as of June 2026, with employment types broken down into 99% Full Time, and 1% Part Time. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $64,877 per year, or $31.2 per hour.
Java Data Reconciliation Engineer- Jersey City

Java Data Reconciliation Engineer- Jersey City

Photon

Jersey City, NJ • On-site

$54 - $74/hr

Full-time

Posted 3 days ago

Job description


Job Title: Java Data Reconciliation Engineer
Summary:
We're seeking a skilled Java Data Reconciliation Engineer to join our team and drive the development of robust data reconciliation solutions within our financial systems. You will be responsible for designing, implementing, and maintaining Java-based applications to perform complex data reconciliations, identify and resolve discrepancies, and automate data matching processes. The ideal candidate possesses strong Java development skills, experience with data reconciliation techniques, and the ability to integrate with diverse data sources and rules engines.
Key Responsibilities:
Data Reconciliation Development:
Design, develop, and test Java-based applications to automate data reconciliation processes across various financial data sources, including relational databases, NoSQL databases, batch files, and real-time data streams.
Implement efficient data transformation, matching algorithm (deterministic and heuristic) with Java and relevant frameworks.
Develop robust error handling and exception management mechanisms to ensure data integrity and system resilience.
Data Analysis and Matching:
Collaborate with business analysts and data architects to understand data requirements and matching criteria.
Analyze and interpret data structures, formats, and relationships to implement effective data matching algorithms in Java.
Rules Engine Integration:
Integrate Java applications with rules engines (e.g., Drools) to implement and execute complex data matching rules.
Develop Java code to interact with the rules engine, manage rule execution, and handle rule-based decision-making.
Problem Solving and Gap Analysis:
Collaborate with cross-functional teams to identify and analyze data gaps and inconsistencies between systems.
Design and develop Java solutions to address data integration challenges and ensure data quality.
Contribute to the development of data governance and quality frameworks within the organization.
Qualifications and Skills:
Bachelor's degree in Computer Science or a related field.
5+ years of hands-on experience in Java development, preferably with exposure to data-intensive applications.
Strong understanding of data reconciliation principles, techniques, and best practices.
Proficiency in Java, Spring Data, and related technologies for data access and integration (e.g., Spring Data, Hibernate, JDBC).
Experience with rules engine integration and development (e.g., Drools).
Strong analytical and problem-solving skills, with the ability to translate business requirements into technical solutions.
Excellent communication and collaboration skills to work effectively with business analysts, data architects, and other team members.
Familiarity with data streaming platforms (e.g., Kafka, Kinesis) and data technologies (e.g., Hadoop, Spark) is a plus.

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