Job Description

Key Responsibilities

1. Data Architecture & Infrastructure

• Design and implement scalable data pipelines for high-volume transaction systems.
• Build and maintain batch and real-time data ingestion frameworks.
• Architect a robust data lake environment for analytics and reporting.
• Optimize database performance for financial-grade accuracy and uptime.
• Ensure the integrity and reconciliation of payment and utility transaction data.
• Design schemas that are optimized for TPV, revenue recognition, settlement tracking, and partner reporting.
• Implement monitoring systems for anomaly detection and transaction inconsistencies.

2. Analytics & Governance

• Build reconciliation frameworks across payment processors, wallets, and settlement reports to ensure financial accuracy.
• Enable BI dashboards for executive, product, performance analytics, and operations teams.
• Enable lending and risk modeling with structured, clean datasets.
• Implement data security, encryption, and access controls.
• Ensure compliance with FinTech and data protection regulations.
• Maintain audit logs and lineage tracking for financial transparency.

3. Collaboration

• Work closely with product, engineering, finance, and risk teams.
• Translate business requirements into scalable data solutions.
• Support experimentation frameworks for growth and product activation.

Technical Requirements

• 3 – 6+ years of experience in data engineering or backend data infrastructure.
• Strong proficiency in SQL and Python.
• Experience with ETL/ELT tools and workflow orchestration (e.g., Airflow or equivalent).
• Experience with cloud platforms (AWS, Azure, or GCP).
• Familiarity with data warehousing solutions (e.g., Snowflake, BigQuery, Redshift, PostgreSQL).
• Strong understanding of database optimization, indexing, and partitioning.

Experience & Key Competencies

• Experience in FinTech, ledger-based architectures, payments, lending, or energy-tech environments.
• Experience with modern data stack tools such as dbt, Airflow, Dagster, or similar orchestration frameworks.
• Familiarity with streaming platforms (Kafka, Kinesis, PubSub).
• Exposure to fraud detection or risk analytics datasets
• Strong analytical mindset
• High ownership and execution bias
• Ability to build for scale, beyond solving immediate problems