Description: Data Engineer (Transaction Analytics Infrastructure)
About Our Team: We specialize in building robust data solutions for modern financial systems, focusing on secure processing and analysis of high-volume transactional datasets. Our work supports enterprise clients in making data-driven decisions for their digital ecosystems.
Core Responsibilities: Architect and maintain high-performance data pipelines for transactional records
Develop optimized ETL processes for complex financial datasets
Implement secure data storage solutions with proper access controls
Create analytical frameworks for transaction pattern recognition
Design monitoring systems for data integrity and pipeline health
Collaborate with analytics teams to deliver actionable insights
Technical Qualifications: 3+ years professional data engineering experience
Expert-level SQL and database optimization skills
Proficiency in Python data stack (Pandas, PySpark, NumPy)
Experience with distributed data processing frameworks
Knowledge of cryptographic data validation techniques
Strong understanding of time-series data management
Familiarity with alternative database structures
Ideal Candidates Have: Background in financial data infrastructure
Experience with immutable data structures
Knowledge of self-verifying data formats
Understanding of high-frequency transaction systems
Engagement Details: Remote work environment
Flexible scheduling options
Project-based or ongoing arrangements
Transparent milestone planning
Application Requirements: Please provide:
Summary of relevant data engineering experience
Examples of previous transactional data projects
Your preferred work structure and availability
We're seeking skilled data professionals who can design systems to handle complex transactional datasets with precision and reliability.
This opportunity is for individual professionals only - agency proposals will not be considered.
