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Job Description & How to Apply Below
Job Title: Java Big Data Engineer
Location: Toronto, ON (Hybrid – 4 days onsite per week)
Employment Type: Contract
About the RoleWe are looking for an experienced Java Big Data Engineer to join a dynamic team in the financial services sector. This is a hybrid role based in Toronto, requiring 4 days onsite per week
. You will work on large-scale data processing pipelines, real-time streaming solutions, and high-performance Java applications.
- Design, develop, and maintain scalable big data pipelines using Java and Spark
- Work with Hadoop ecosystems (HDFS, Hive, HBase) to process and store large volumes of data
- Implement real-time streaming solutions using Kafka and Spark Streaming
- Optimize and tune performance of big data applications for efficiency and reliability
- Collaborate with data scientists, analysts, and business stakeholders to understand data requirements
- Write clean, maintainable, and testable code following best practices
- Participate in code reviews, design discussions, and agile ceremonies
- Troubleshoot and resolve production issues related to data processing and integration
- Ensure data quality, governance, and security standards are met
- Work with cloud-based data platforms (preferably Azure or AWS)
- Support and maintain existing big data workflows and ETL processes
- Strong core Java (8+) development experience
- Hands-on experience with Apache Spark (batch and streaming)
- Proficiency in Hadoop ecosystem (HDFS, Hive, YARN)
- Experience with Kafka for real-time data streaming
- Strong SQL skills and experience with relational and No
SQL databases - Experience with Unix/Linux environments and shell scripting
- Familiarity with version control (Git) and CI/CD pipelines
- Experience with Azure (Data Lake, Databricks, Synapse) or AWS (EMR, S3, Redshift)
- Knowledge of Scala or Python
- Understanding of containerization (Docker, Kubernetes)
- Experience with scheduling tools (Airflow, Oozie)
- Financial services or banking domain experience
- Knowledge of data warehousing concepts and tools
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