Key Responsibilities

Data Architecture &amp; Platform Design

<ul>
<li style="margin-left:0px;">Define and own the end-to-end data architecture.</li>
<li></li>
<li style="margin-left:0px;">Design canonical data models for:</li>
</ul>

<ul>
<li style="margin-left:0px;">Remittances</li>
</ul>

<ul>
<li style="margin-left:0px;">Payments</li>
</ul>

<ul>
<li style="margin-left:0px;">Invoices</li>
</ul>

<ul>
<li style="margin-left:0px;">Customers</li>
</ul>

<ul>
<li style="margin-left:0px;">Deductions &amp; adjustments</li>
</ul>

<ul>
<li style="margin-left:0px;">Branding &amp; template configurations</li>
</ul>

<ul>
<li style="margin-left:0px;">Establish scalable data lake/warehouse strategies.</li>
</ul>

Data Pipeline Engineering

<ul>
<li style="margin-left:0px;">Build and maintain batch and real-time pipelines ingesting data from:</li>
</ul>

<ul>
<li style="margin-left:0px;">ERP systems (SAP, Oracle, NetSuite, etc.)</li>
</ul>

<ul>
<li style="margin-left:0px;">Banking systems</li>
</ul>

<ul>
<li style="margin-left:0px;">Email ingestion &amp; EDI feeds</li>
</ul>

<ul>
<li style="margin-left:0px;">Customer portals &amp; SFTP</li>
</ul>

<ul>
<li style="margin-left:0px;">Ensure reliable data transformation, normalization, and enrichment.</li>
</ul>

<ul>
<li style="margin-left:0px;">Support structured outputs consumed by AI and integration layers.</li>
</ul>

Data for AI &amp; Automation

<ul>
<li style="margin-left:0px;">Design data pipelines that power:</li>
</ul>

<ul>
<li style="margin-left:0px;">Intelligent document processing</li>
</ul>

<ul>
<li style="margin-left:0px;">Matching &amp; reconciliation models</li>
</ul>

<ul>
<li style="margin-left:0px;">Exception detection engines</li>
</ul>

<ul>
<li style="margin-left:0px;">Ensure high-quality labeled datasets and feature engineering frameworks.</li>
</ul>

<ul>
<li style="margin-left:0px;">Enable feedback loops for model improvement.</li>
</ul>

Data Quality, Governance &amp; Security

<ul>
<li style="margin-left:0px;">Implement data validation, reconciliation, and monitoring frameworks.</li>
</ul>

<ul>
<li style="margin-left:0px;">Ensure financial data accuracy and consistency across systems.</li>
</ul>

<ul>
<li style="margin-left:0px;">Define audit trails, lineage, and compliance controls.</li>
</ul>

<ul>
<li style="margin-left:0px;">Enforce role-based access and data security best practices.</li>
</ul>

Reporting &amp; Analytics Enablement

<ul>
<li style="margin-left:0px;">Enable KPI dashboards for:</li>
</ul>

<ul>
<li style="margin-left:0px;">Auto-match rates</li>
</ul>

<ul>
<li style="margin-left:0px;">Exception trends</li>
</ul>

<ul>
<li style="margin-left:0px;">Payment reconciliation metrics</li>
</ul>

<ul>
<li style="margin-left:0px;">Support analytics for finance and operations teams.</li>
</ul>

<ul>
<li style="margin-left:0px;">Optimize data availability for near real-time reporting.</li>
</ul>

Leadership &amp; Collaboration

<ul>
<li style="margin-left:0px;">Lead a team of data engineers.</li>
</ul>

<ul>
<li style="margin-left:0px;">Partner with AI, Integration, ERP, and Product teams.</li>
</ul>

<ul>
<li style="margin-left:0px;">Drive data roadmap aligned with automation and scalability goals.</li>
</ul>

<ul>
<li style="margin-left:0px;">Conduct design reviews and enforce data standards.</li>
</ul>

Required Qualifications

<ul>
<li style="margin-left:0px;">8–14+ years of experience in data engineering or data architecture.</li>
</ul>

<ul>
<li style="margin-left:0px;">Strong expertise in:</li>
</ul>

<ul>
<li style="margin-left:0px;">Data pipeline frameworks (Airflow, Spark, Kafka, etc.)</li>
</ul>

<ul>
<li style="margin-left:0px;">Cloud platforms (AWS, Azure, GCP)</li>
</ul>

<ul>
<li style="margin-left:0px;">Data warehouses (Snowflake, Redshift, BigQuery, etc.)</li>
</ul>

<ul>
<li>SQL and Python</li>
</ul>

<ul>
<li style="margin-left:0px;">Experience handling high-volume enterprise data.</li>
</ul>

<ul>
<li style="margin-left:0px;">Strong understanding of data modeling and ETL/ELT processes.</li>
</ul>

Preferred Experience

<ul>
<li style="margin-left:0px;">Financial systems, AR, or ERP-integrated platforms.</li>
</ul>

<ul>
<li style="margin-left:0px;">Remittance and payment reconciliation workflows.</li>
</ul>

<ul>
<li style="margin-left:0px;">AI/ML data pipeline support.</li>
</ul>

<ul>
<li style="margin-left:0px;">Real-time event-driven architectures.</li>
</ul>

<ul>
<li style="margin-left:0px;">Data governance in regulated environments.</li>
</ul>

L6– Senior Data Engineering Lead
<ul>
<li style="margin-left:0px;">Owns execution and reliability of data platform.</li>
</ul>

<ul>
<li style="margin-left:0px;">Leads engineering delivery and optimization.</li>
</ul>

<ul>
<li style="margin-left:0px;">Ensures data quality and production stability.</li>
</ul>

<ul>
<li style="margin-left:0px;">Defines long-term enterprise data architecture.</li>
</ul>

<ul>
<li style="margin-left:0px;">Drives cross-product data strategy and governance.</li>
</ul>

<ul>
<li style="margin-left:0px;">Influences executive-level data decisions.</li>
</ul>

What Success Looks Like

<ul>
<li style="margin-left:0px;">Highly reliable and scalable data pipelines.</li>
</ul>

<ul>
<li style="margin-left:0px;">Accurate financial data reconciliation across systems.</li>
</ul>

<ul>
<li style="margin-left:0px;">Strong data quality with minimal inconsistencies.</li>
</ul>

<ul>
<li style="margin-left:0px;">AI-ready datasets with high usability.</li>
</ul>

<ul>
<li style="margin-left:0px;">Timely delivery of reporting and analytics. </li>
</ul>

Data Engineering

India

Key Responsibilities Data Architecture &amp; Platform Design Define and own the end-to-end data architecture. Design canonical data models for: Remittances Payments Invoices Customers Deductions &amp; adjustments Branding &amp; 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 &amp; EDI feeds Customer portals &amp; SFTP Ensure reliable data transformation, normalization, and enrichment. Support structured outputs consumed by AI and integration layers. Data for AI &amp; Automation Design data pipelines that power: Intelligent document processing Matching &amp; reconciliation models Exception detection engines Ensure high-quality labeled datasets and feature engineering frameworks. Enable feedback loops for model improvement. Data Quality, Governance &amp; 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 &amp; 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 &amp; 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.

Data Engineering · Mumbai

Job highlights

Skillset required

Job Description

Job Description for Data Engineering

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