About the role
Role Name: Big Data Developer Location: Toronto 4 days a week on site,
JOB DESCRIPTION: "Primary We are seeking a Big Data Developer to build and enhance realtime and batch fraud analytics AML platforms that process highvolume financial transactions The role focuses on developing scalable data pipelines that support transaction monitoring alert generation investigations and regulatory compliance Key Responsibilities Design and build realtime and batch data pipelines for fraud detection and AML monitoring Process streaming transaction data using Apache Spark and Kafka Develop ETLELT workflows supporting customer transaction and risk datasets Ensure data quality traceability and audit readiness to meet regulatory requirements Optimize performance for lowlatency highthroughput fraud use cases Support alert data generation and downstream analyticsinvestigation platforms Monitor and troubleshoot production data pipelines in secure environments Collaborate with fraud risk compliance and downstream application teams
Required Skills Strong experience with Apache Spark Core SQL Streaming Handson expertise in Kafka or realtime event streaming frameworks Hadoop ecosystem HDFS Hive Programming in Python Scala Java Strong SQL and dataprocessing skills Experience working in LinuxUnix environments
Preferred Skills Experience in fraud detection AML transaction monitoring or CAMPS systems Exposure to banking regulatory environments Cloud platforms Azure AWS NoSQL databases HBase Cassandra MongoDB Workflow orchestration tools Airflow Oozie"
Similar Jobs
About the role
Role Name: Big Data Developer Location: Toronto 4 days a week on site,
JOB DESCRIPTION: "Primary We are seeking a Big Data Developer to build and enhance realtime and batch fraud analytics AML platforms that process highvolume financial transactions The role focuses on developing scalable data pipelines that support transaction monitoring alert generation investigations and regulatory compliance Key Responsibilities Design and build realtime and batch data pipelines for fraud detection and AML monitoring Process streaming transaction data using Apache Spark and Kafka Develop ETLELT workflows supporting customer transaction and risk datasets Ensure data quality traceability and audit readiness to meet regulatory requirements Optimize performance for lowlatency highthroughput fraud use cases Support alert data generation and downstream analyticsinvestigation platforms Monitor and troubleshoot production data pipelines in secure environments Collaborate with fraud risk compliance and downstream application teams
Required Skills Strong experience with Apache Spark Core SQL Streaming Handson expertise in Kafka or realtime event streaming frameworks Hadoop ecosystem HDFS Hive Programming in Python Scala Java Strong SQL and dataprocessing skills Experience working in LinuxUnix environments
Preferred Skills Experience in fraud detection AML transaction monitoring or CAMPS systems Exposure to banking regulatory environments Cloud platforms Azure AWS NoSQL databases HBase Cassandra MongoDB Workflow orchestration tools Airflow Oozie"