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Requirements:
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 d ata 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:
* Modern data stack tools- DBT, DuckDB, data orchestration tools (e.g., Apache Airflow, Dagster) - 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
Our Story
 A leading force in fintech innovation, tackling the pervasive issue of chargeback fraud that undermines online businesses. Born from a deep passion for technology and a commitment to excel in Ecommerce and fintech, we've developed an AI-driven solution aimed at combating the frustrations of credit card disputes.