Data Engineering · Bengaluru

Key Responsibilities

Data Architecture & Platform Design

• Define and own the end-to-end data architecture.
• Design canonical data models for:

• Remittances

• Payments

• Invoices

• Customers

• Deductions & adjustments

• Branding & template configurations

• Establish scalable data lake/warehouse strategies.

Data Pipeline Engineering

• Build and maintain batch and real-time pipelines ingesting data from:

• ERP systems (SAP, Oracle, NetSuite, etc.)

• Banking systems

• Email ingestion & EDI feeds

• Customer portals & SFTP

• Ensure reliable data transformation, normalization, and enrichment.

• Support structured outputs consumed by AI and integration layers.

Data for AI & Automation

• Design data pipelines that power:

• Intelligent document processing

• Matching & reconciliation models

• Exception detection engines

• Ensure high-quality labeled datasets and feature engineering frameworks.

• Enable feedback loops for model improvement.

Data Quality, Governance & Security

• Implement data validation, reconciliation, and monitoring frameworks.

• Ensure financial data accuracy and consistency across systems.

• Define audit trails, lineage, and compliance controls.

• Enforce role-based access and data security best practices.

Reporting & Analytics Enablement

• Enable KPI dashboards for:

• Auto-match rates

• Exception trends

• Payment reconciliation metrics

• Support analytics for finance and operations teams.

• Optimize data availability for near real-time reporting.

Leadership & Collaboration

• Lead a team of data engineers.

• Partner with AI, Integration, ERP, and Product teams.

• Drive data roadmap aligned with automation and scalability goals.

• Conduct design reviews and enforce data standards.

Required Qualifications

• 8–14+ years of experience in data engineering or data architecture.

• Strong expertise in:

• Data pipeline frameworks (Airflow, Spark, Kafka, etc.)

• Cloud platforms (AWS, Azure, GCP)

• Data warehouses (Snowflake, Redshift, BigQuery, etc.)

• SQL and Python

• Experience handling high-volume enterprise data.

• Strong understanding of data modeling and ETL/ELT processes.

Preferred Experience

• Financial systems, AR, or ERP-integrated platforms.

• Remittance and payment reconciliation workflows.

• AI/ML data pipeline support.

• Real-time event-driven architectures.

• Data governance in regulated environments.

L6– Senior Data Engineering Lead

• Owns execution and reliability of data platform.

• Leads engineering delivery and optimization.

• Ensures data quality and production stability.

• Defines long-term enterprise data architecture.

• Drives cross-product data strategy and governance.

• Influences executive-level data decisions.

What Success Looks Like

• Highly reliable and scalable data pipelines.

• Accurate financial data reconciliation across systems.

• Strong data quality with minimal inconsistencies.

• AI-ready datasets with high usability.

• Timely delivery of reporting and analytics.