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Data Reconciliation Jobs (NOW HIRING)

Photon

Java/API Lead - Jersey City

Jersey City, NJ · On-site

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 ...

Photon

Java/API Lead - Jersey City

Jersey City, NJ · On-site

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 ...

Neotecra

Data Mapping GIS analyst

New York, NY · On-site

Validate and reconcile transit data from GTFS feeds, BusTime, internal databases, and field observations * Identify and resolve duplicate or conflicting stop IDs, route IDs, and location information

USM

JB061757 - HEDIS Data Analyst (SQL)

Philadelphia, PA · On-site +1

Perform data reconciliation, quality assurance, and root cause analysis of data discrepancies. * Support annual HEDIS submission activities, including data validation and audit preparation. * Create ...

1 point system

Data QA

New York, NY · Remote

$117K - $140K/yr

Perform regression testing, data reconciliation, and validation of large datasets. * Document test cases, results, and defects in compliance with QA standards. * Contribute to continuous improvement ...

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How much do data reconciliation jobs pay per hour?

As of Aug 12, 2026, the average hourly pay for data reconciliation in the United States is $21.84, according to ZipRecruiter salary data. Most workers in this role earn between $18.99 and $24.62 per hour, depending on experience, location, and employer.

What is a data reconciliation analyst job description?

A data reconciliation analyst is responsible for comparing and verifying data from different sources to ensure accuracy and consistency. They identify discrepancies, investigate causes, and implement corrective actions, often using tools like Excel, SQL, or data management software. Strong attention to detail and analytical skills are essential for this role.

What are the typical daily responsibilities of a data reconciliation professional?

A Data Reconciliation professional is responsible for comparing and verifying data across different systems or sources to identify and resolve discrepancies. Day-to-day tasks may include reviewing transaction records, preparing reconciliation reports, following up on unmatched items, and collaborating with internal departments such as accounting, operations, or IT for resolution. They also document reconciliation processes and may recommend improvements to increase accuracy and efficiency. This role often requires managing tight deadlines and maintaining high standards of data quality, making strong organizational skills essential.

What are the key skills and qualifications needed to thrive in data reconciliation, and why are they important?

To thrive in a Data Reconciliation role, you need strong analytical abilities, attention to detail, and a solid understanding of data management principles, often supported by a degree in finance, accounting, or a related field. Experience with reconciliation software, ERP systems, advanced Excel functions, and sometimes certifications in accounting or data analysis are highly valued. Excellent problem-solving skills, effective communication, and a collaborative attitude set top performers apart. These competencies ensure accurate data matching, quick resolution of discrepancies, and the integrity of financial or operational records.

Is data reconciliation a good career?

Data reconciliation is a valuable career in finance, accounting, and data management, involving comparing and matching data from different sources to ensure accuracy. It requires attention to detail, proficiency with spreadsheets and database tools, and often offers stable employment with opportunities for advancement. The role is essential in industries that rely on precise data, such as banking, insurance, and auditing.

What is a data reconciliation?

A Data Reconciliation job involves comparing and verifying data from different sources to ensure accuracy and consistency. Professionals in this role identify discrepancies, correct errors, and ensure that records align across databases, financial reports, or operational systems. This process is crucial in industries like finance, healthcare, and logistics to maintain data integrity and compliance with regulations. Strong analytical skills, attention to detail, and proficiency in data management tools are essential for success in this role.

More about Data Reconciliation jobs
What cities are hiring for Data Reconciliation jobs? Cities with the most Data Reconciliation job openings:
What are the most commonly searched types of Data Reconciliation jobs? The most popular types of Data Reconciliation jobs are:
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Data Reconciliation jobs near you
Infographic showing various Data Reconciliation 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 $45,429 per year, or $21.8 per hour.

Java/API Lead - Jersey City

Photon

Jersey City, NJ • On-site

Full-time

Re-posted 20 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. 

9+ 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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