Reconciliation Data Analyst

As a Reconciliation Data Analyst, you will work at the intersection of financial operations, payment insight analysis, large-scale data processing, Tableau report building and AI/ML-driven anomaly detection. You will be responsible for performing high-volume reconciliations and analysis between internal ledgers, payment pipelines, and banking partners. Additionally, you will apply machine learning techniques to detect mismatches, predict anomalies, and support proactive financial risk management.

This is a critical data-focused analysis role for analysts who are equally comfortable with Spark SQL, Python, Big Data platforms, and foundational AI/ML tools.

Job Responsibilities

Reconciliation & Data Integrity

• Execute automated reconciliations between payment gateways, banking settlements, and internal transaction records.
• Maintain reconciliation SLAs (e.g., T+1, T+3 variance resolution) and build recon dashboards for daily break monitoring.
• Build and monitor dashboards for break detection, aging summaries, and resolution tracking.
• Use spark SQL queries and semi-structured data extraction for deep reconciliation pipelines.

Data Engineering & Automation

• Write scalable Hive/BigQuery queries to do RCA automation
• Build web tool using Python scripts to reduce manual work and data research.
• Develop and maintain recon tools and workflows using orchestration platforms (e.g., Airflow, DLS).
• Support pipeline enhancements by collaborating with engineering teams to optimize recon job performance.

Reporting & Audit

• Provide payment insights to support business decision and monitoring
• Present analysis findings and root cause summaries to finance controllers and auditors.
• Ensure data integrity in SOX-auditable recon processes; assist with documentation for internal controls.
• Support audit and compliance teams with automated variance tracking logs and break classification logic.

AI/ML-Driven Anomaly Detection & Forecasting

• Build lightweight ML models (e.g., classification, outlier detection) to provide issue summary, RCA of exception, payment data insight.
• Implement anomaly detection frameworks
• Work closely with data scientists and engineering to deploy detection pipelines within scheduled recon workflows.

Qualifications & Skills

Required:

• 4–7 years in data analytics, reconciliation, financial operations, or payments analytics
• Proficient in SQL, Hive/Presto, Python (Pandas, NumPy)
• Experience with Hadoop, Spark, or cloud-scale data platforms
• Ability to work independently with large-scale financial data systems
• Strong skill of Tableau or other BI tools

Preferred:

• Working knowledge of ML algorithms applied to anomaly detection or classification problems is plus
• Familiarity with financial data flows: settlements, payments, general ledger, etc.
• Understanding of ledger reconciliations, journal entries, and financial compliance