Our ultimate goal is to equip our clients with resilient safeguards against chargebacks, empowering them to safeguard their revenue and optimize their profitability. Join us on this thrilling mission to redefine the battle against fraud.
Your Arena:
Design, develop, and maintain scalable, robust data pipelines and ETL processes.
Architect and implement complex data models across various storage solutions.
Collaborate with R&D teams, data scientists, analysts, and other stakeholders to understand data requirements and deliver high-quality solutions.
Ensure data quality, consistency, security, and compliance across all data systems.
Play a key role in defining and implementing data strategies that drive business value.
Contribute to the continuous improvement of our data architecture and processes.
Champion and implement data engineering best practices across the R&D organization, serving as a technical expert and go-to resource for data-related questions and challenges.
Participate in and sometimes lead code reviews to maintain high coding standards.
Troubleshoot and resolve complex data-related issues in production environments.
Evaluate and recommend new technologies and methodologies to improve our data infrastructure.
What It Takes – Must haves:
5+ years of experience in data engineering, with specific, strong proficiency in Python & software engineering principles – Must.
Extensive experience with AWS, GCP, Azure and cloud-native architectures – Must.
Deep knowledge of both relational (e.g., PostgreSQL) and NoSQL databases – Must.
Designing and implementing data warehouses and data lakes – Must
Strong understanding of data modeling techniques – Must.
Expertise in data manipulation libraries (e.g., Pandas) and big data processing frameworks – Must.
Experience with data validation tools such as Pydantic & Great Expectations – Must.
Proficiency in writing and maintaining unit tests (e.g., Pytest) and integration tests – Must.
Advantages:
Apache Iceberg – Experience building, managing and maintaining Iceberg lakehouse architecture with S3 storage and AWS Glue catalog – Strong Advantage.
Apache Spark – Proficiency in optimizing Spark jobs, understanding partitioning strategies, and leveraging core framework capabilities for large-scale data processing – Strong Advantage.
Modern data stack tools – DBT, DuckDB, Dagster or any other Data orchestration tool (e.g., Apache Airflow, Prefect) – Advantage.
Designing and developing backend systems, including- RESTful API design and implementation, microservices architecture, event-driven systems, RabbitMQ, Apache Kafka – Advantage.
Containerization technologies- Docker, Kubernetes, and IaC (e.g., Terraform) – Advantage.
Stream processing technologies (e.g., Apache Kafka, Apache Flink) – Advantage.
Understanding of compliance requirements (e.g., GDPR, CCPA) – Advantage
Experience mentoring junior engineers or leading small project teams.
Excellent communication skills with the ability to explain complex technical concepts to various audiences.
Demonstrated ability to work independently and lead technical initiatives
Relevant certifications in cloud platforms or data technologies.
