Software Engineer

Scotiabank•Toronto, ON

13d•Onsite

About The Position

As a Software Engineer (ETL Development focus) within Client Systems, Canadian Banking Engineering team, you will play a key role in designing, developing, and modernizing enterprise-grade data integration platforms. This includes building scalable batch and distributed data pipelines, enabling high-performance data processing using Spark, and supporting the transformation of legacy ETL workloads into modern, cloud-ready architectures. This role is ideal for someone who thrives in large-scale data transformation programs, enjoys working on high-volume data pipelines, and is motivated to deliver reliable, performant, and future-ready data integration solutions.

Requirements

Strong hands-on experience with Talend ETL development
Strong experience with Apache Spark (PySpark or Scala) for large-scale data processing
Experience working with distributed data processing and big data frameworks
Strong Unix/Linux scripting experience for ETL orchestration and automation
Strong experience with relational databases (DB2, Oracle, or similar)
Advanced SQL proficiency: Complex transformations, Performance tuning, Stored procedures
Experience working with large datasets and data warehousing concepts
Proven experience in enterprise ETL/data pipeline development
Experience with data migration and modernization initiatives (legacy → distributed platforms)
Experience with Batch processing frameworks
Experience with Data ingestion and transformation pipelines
Experience with Messaging systems (Kafka, MQ)
Experience with API-based integrations
Experience with source control systems (Git, Bitbucket, GitHub)
Hands-on experience supporting ETL modernization or platform migration programs
Experience migrating from traditional ETL tools (e.g. Talend, Informatica, DataStage) to Spark or cloud-based data platforms
Understanding of data pipeline re-engineering, re-platforming, and performance optimization strategies

Nice To Haves

Experience with cloud data platforms (Azure, AWS, or GCP)
Familiarity with data orchestration tools (Airflow or similar)
Exposure to real-time/streaming data processing (Spark Streaming, Kafka Streams)
Knowledge of data governance, lineage, and metadata management
Experience in data quality frameworks and reconciliation methodologies
Exposure to event-driven data architectures
Experience working with customer or financial data domains is an asset
Experience in financial services or regulated environments is preferred

Responsibilities

Design, develop, and support scalable ETL/data pipelines using tools such as Talend and Apache Spark framework
Lead and contribute to the migration of legacy ETL workloads to modern frameworks (e.g. Spark )
Build and optimize large-scale batch and distributed data processing pipelines
Analyze ETL performance (CPU, memory, I/O, runtime bottlenecks) and implement tuning strategies
Develop reusable ETL frameworks, components, and orchestration patterns for enterprise use
Implement data ingestion, transformation, and data quality checks across structured and semi-structured data sources
Develop and maintain complex SQL transformations, stored procedures, and data models
Integrate ETL pipelines with enterprise systems using messaging (Kafka/MQ), APIs, and batch orchestration frameworks
Ensure data integrity, lineage, reconciliation, and auditability across pipelines
Collaborate with architecture and engineering teams to design target-state data platforms and migration approaches
Participate in end-to-end system integration and migration testing across distributed platforms
Contribute to technical design discussions and provide input to stakeholders
Collaborate with cross-functional teams including data engineering, application support, database, and infrastructure teams
Mentor junior developers and promote best practices in ETL design, performance tuning, and data engineering
Ensure adherence to coding standards, version control, and CI/CD practices (Git-based repositories)