About the Role
We are looking for a hands-on Python Developer with Spark expertise to join our Fraud Technology team. This is a mid-level individual contributor role focused on building scalable, production-grade data solutions that support real-time fraud detection systems. The ideal candidate will bring strong data engineering skills, a solid grasp of software design principles, and the ability to evaluate and integrate modern data platforms into a complex enterprise ecosystem.
Key Responsibilities
Design, develop, and optimize scalable data pipelines using Python and Apache Spark .
Build high-performance data workflows with pushdown optimization, streaming, replication, partitioning, and clustering techniques.
Evaluate and integrate modern data platforms and tools into our enterprise architecture.
Engineer and manage feature pipelines to support real-time fraud detection systems.
Design data models and processing strategies that align with distributed system principles, ensuring scalability, consistency, and performance across large-scale environments.
Develop solutions that are production-ready, maintainable, and built with observability and operational excellence in mind.
Apply clean code practices, SOLID principles, and architecture patterns to deliver robust and extensible systems.
Participate in code reviews, testing, and deployment activities.
Contribute to architectural decisions and continuous improvement initiatives.
Required Skills
3+ years of professional experience in Python development with a strong focus on data engineering.
Hands-on expertise with Apache Spark and Big Data processing .
Strong SQL skills and experience with both relational and distributed data systems .
Solid understanding of software engineering principles , clean code , and design patterns .
Experience in system design and architecture for scalable, data-intensive applications.
Ability to evaluate new technologies and recommend solutions aligned with enterprise needs.
Excellent analytical and problem-solving skills.
Strong communication and collaboration abilities.
Beneficial Skills (Nice to have)
Exposure to cloud platforms such as Snowflake , Databricks , or similar.
Familiarity with Kafka, Redis, Airflow, Jenkins , and Git .
Understanding of observability practices and tools like Open Telemetry, Grafana, Loki , or Tempo , with a mindset for embedding observability into system design.
Awareness of Kubernetes, Helm, GitOps , and containerization concepts.
Background in real-time systems , or financial services domains.
Good sense of humor
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We are looking for a hands-on Python Developer with Spark expertise to join our Fraud Technology team. This is a mid-level individual contributor role focused on building scalable, production-grade data solutions that support real-time fraud detection systems. The ideal candidate will bring strong data engineering skills, a solid grasp of software design principles, and the ability to evaluate and integrate modern data platforms into a complex enterprise ecosystem.
Key Responsibilities
Design, develop, and optimize scalable data pipelines using Python and Apache Spark .
Build high-performance data workflows with pushdown optimization, streaming, replication, partitioning, and clustering techniques.
Evaluate and integrate modern data platforms and tools into our enterprise architecture.
Engineer and manage feature pipelines to support real-time fraud detection systems.
Design data models and processing strategies that align with distributed system principles, ensuring scalability, consistency, and performance across large-scale environments.
Develop solutions that are production-ready, maintainable, and built with observability and operational excellence in mind.
Apply clean code practices, SOLID principles, and architecture patterns to deliver robust and extensible systems.
Participate in code reviews, testing, and deployment activities.
Contribute to architectural decisions and continuous improvement initiatives.
Required Skills
3+ years of professional experience in Python development with a strong focus on data engineering.
Hands-on expertise with Apache Spark and Big Data processing .
Strong SQL skills and experience with both relational and distributed data systems .
Solid understanding of software engineering principles , clean code , and design patterns .
Experience in system design and architecture for scalable, data-intensive applications.
Ability to evaluate new technologies and recommend solutions aligned with enterprise needs.
Excellent analytical and problem-solving skills.
Strong communication and collaboration abilities.
Beneficial Skills (Nice to have)
Exposure to cloud platforms such as Snowflake , Databricks , or similar.
Familiarity with Kafka, Redis, Airflow, Jenkins , and Git .
Understanding of observability practices and tools like Open Telemetry, Grafana, Loki , or Tempo , with a mindset for embedding observability into system design.
Awareness of Kubernetes, Helm, GitOps , and containerization concepts.
Background in real-time systems , or financial services domains.
Good sense of humor
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