Software Engineer (Mississauga)

Required Skills & Experience

Expertise in big data technologies (Hadoop, Spark, Kafka) Advanced SQL proficiency for investigation and optimization Strong Python development skills Experience with cloud platforms (AWS or Azure) Knowledge of Snowflake and dimensional modeling Experience with Dev Ops tooling (CI/CD, Docker, Kubernetes) Familiarity with ML frameworks (Tensor Flow, PyTorch) Strong debugging and analytical skills Nice to Have Skills & Experience

Leadership and mentorship Effective communication with technical and non‑technical audiences Problem‑solving and critical thinking Ownership and accountability Adaptability and continuous learning Job Description

The Data Engineer is responsible for architecting, developing, and maintaining enterprise‑scale data platforms that support analytics, operational reporting, and machine learning initiatives. This role requires deep technical expertise in distributed systems, cloud platforms, and data modeling, combined with strong communication and leadership capabilities. Design and implement large‑scale ETL/ELT pipelines using Python, Spark, and distributed processing frameworks Develop and maintain big data infrastructure leveraging Hadoop, Spark, Kafka, and Kafka Streams Architect cloud‑native data solutions on AWS or Azure, including serverless components Build and optimize Snowflake data warehouses using dimensional modeling best practices Conduct data discovery and source analysis to support recent integrations and transformations Model complex datasets using normalized, denormalized, star, and snowflake schemas Integrate external systems through RESTful APIs and automated ingestion frameworks Implement Dev Ops practices including CI/CD, containerization, and orchestration Develop real‑time streaming applications using Kafka, Storm, Kinesis, or Pub/Sub Operationalize machine learning models in collaboration with data science teams Optimize performance across queries, pipelines, and distributed workloads Troubleshoot and resolve complex data issues across upstream and downstream systems Maintain comprehensive documentation for data pipelines, models, and integrations

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