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Data Reconciliation Jobs (NOW HIRING)

BCForward

Data Analyst

New York, NY · Remote

$30 - $42/hr

This role involves collecting, validating, reconciling, and maintaining workforce data across multiple enterprise systems including ERP, procurement, AP, and vendor management platforms. The ideal ...

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How much do data reconciliation jobs pay per hour?

As of Jun 7, 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 are 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 the Data Reconciliation position, 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.

What is a Data Reconciliation job?

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.

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Data Reconciliation jobs near you
SPARK Data Reconciliation Engineer- NJ

SPARK Data Reconciliation Engineer- NJ

Photon

Addison, TX • On-site

Full-time

Posted 3 days ago

Job description

Job Description
Job Title: PySpark Data Reconciliation Engineer
Summary:
We're seeking a skilled PySpark 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 PySpark-based applications to perform complex data reconciliations, identify and resolve discrepancies, and automate data matching processes. The ideal candidate possesses strong PySpark 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 PySpark-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 algorithms (deterministic and heuristic) using PySpark and relevant big data frameworks.
  • Develop robust error handling and exception management mechanisms to ensure data integrity and system resilience within Spark jobs.

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 using PySpark.
  • Work with distributed datasets in Spark, ensuring optimal performance for large-scale data reconciliation.

Rules Engine Integration:
  • Integrate PySpark applications with rules engines (e.g., Drools) or equivalent to implement and execute complex data matching rules.
  • Develop PySpark 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 PySpark-based 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 big data development, preferably with exposure to data-intensive applications.
  • Strong understanding of data reconciliation principles, techniques, and best practices.
  • Proficiency in PySpark, Apache Spark, and related big data technologies for data processing and integration.
  • Experience with rules engine integration and development
  • 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 big data technologies (e.g., Hadoop, Hive, HBase) is a plus.

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