Data Engineer - BFSI Cloud & Payments Platform

Role :Data Engineer - BFSI Cloud & Payments Platform

Location :Charlotte, NC (Hybrid)

Rate: $65/hr - $70/hr

Role Summary

We are seeking an experiencedData Engineer to support the design, development, and optimization of data ingestion, transformation, enrichment, reconciliation, and transaction correlation capabilities for Banking and Financial Services (BFSI) platforms. The role requires expertise in ETL/ELT development, event-driven data processing, data quality frameworks, and transaction lifecycle analysis to enable accurate transaction tracing, observability, and analytics across complex banking ecosystems.

Key Responsibilities

Data Engineering & Platform Development

• Design and develop ETL/ELT pipelines for structured and semi-structured data sources.
• Build scalable ingestion, transformation, and enrichment frameworks.
• Develop reusable data processing components and engineering utilities.
• Support onboarding of new source systems and data domains.
• Implement data engineering best practices, standards, and coding guidelines.
• Contribute to modernization of legacy data integration processes.

BFSI Data Processing & Transaction Support

Support data solutions across:

• Core Banking Platforms
• Digital Banking Systems
• Payments Platforms (ACH, RTP, FedNow, SWIFT, ISO 20022)
• ATM Transaction Processing Systems
• Fraud Monitoring Platforms
• Treasury & Cash Management Systems
• Lending Platforms
• Trade Finance Systems

Key responsibilities include:

• Develop transaction transformation and enrichment logic.
• Support transaction correlation and traceability requirements.
• Perform transaction lifecycle and source system analysis.
• Build reconciliation and exception-handling workflows.
• Support canonical transaction and event data models.
• Enable transaction auditability and operational visibility.

Data Quality, Validation & Reconciliation

• Implement data validation, quality, and reconciliation frameworks.
• Develop automated checks for data completeness, consistency, and accuracy.
• Identify missing transactions, duplicate events, and data anomalies.
• Support reconciliation processes across upstream and downstream systems.
• Define and monitor data quality KPIs and controls.
• Assist in root-cause analysis and issue resolution activities.

Security, Governance & Compliance

• Implement secure data processing and storage practices.
• Support compliance with:
  • PCI-DSS
  • GDPR
  • SOC 2
  • ISO 27001
  • FFIEC Guidelines
  • RBI Regulations
• Ensure compliance with enterprise data governance standards.
• Support metadata management, lineage tracking, and audit requirements.
• Implement data masking and sensitive data protection controls.
• Maintain data traceability and regulatory reporting readiness.

Cloud Engineering & DevOps

• Support deployment of data solutions on cloud platforms.
• Contribute to CI/CD implementation and deployment automation.
• Develop data processing workflows for cloud-native environments.
• Support containerized data workloads and orchestration platforms.
• Participate in performance optimization and operational support activities.
• Follow DevSecOps and automation best practices.

Data Warehousing, Analytics & Event Processing

• Develop and maintain data pipelines supporting data warehouses and data lakes.
• Support event-driven architectures and streaming data integrations.
• Build curated datasets for analytics, reporting, and investigation use cases.
• Prepare data for operational dashboards and transaction monitoring solutions.
• Support real-time and batch processing requirements.
• Enable downstream analytics and business intelligence platforms.

Leadership & Stakeholder Management

• Collaborate with architects, senior data engineers, backend engineers, and business stakeholders.
• Participate in technical design reviews and sprint planning activities.
• Support effort estimation and implementation planning.
• Contribute to engineering standards and documentation.
• Share knowledge and mentor junior team members when required.
• Drive continuous improvement initiatives across data engineering processes.

Required Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Technology, or related field.
• 5+ years of experience in Data Engineering and Data Integration.
• Strong hands-on expertise with:
  • SQL
  • Python
  • Apache Spark / PySpark
  • ETL/ELT Development
  • Data Warehousing Concepts
• Experience building data transformation and enrichment pipelines.
• Knowledge of event-driven architectures and data processing frameworks.
• Experience implementing data quality and validation solutions.
• Strong analytical and problem-solving skills.
• Experience working in Agile development environments.

Technical & Domain Knowledge

Preferred Qualifications

• Banking and Financial Services domain experience.
• Experience working with banking data platforms and transaction processing systems.
• Exposure to payments ecosystems including ACH, RTP, SWIFT, ISO 8583, and ISO 20022.
• Experience supporting reconciliation and transaction traceability initiatives.
• Familiarity with Kafka, CDC frameworks, and streaming data architectures.
• Experience with operational reporting, observability, or analytics platforms.
• Cloud platform certifications are a plus.

Success Measures

• Reliable and scalable ETL/ELT data pipelines.
• Accurate transaction enrichment, transformation, and correlation.
• High data quality and reconciliation accuracy.
• Efficient onboarding of new source systems and datasets.
• Timely delivery of curated datasets for analytics and investigations.
• Improved transaction traceability and operational visibility.
• Secure, compliant, and maintainable data engineering solutions.
• Successful support of TTU and banking modernization initiatives.