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
  

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.
  

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.
  

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.