Data Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Engineer II based in Canada. This role sits within a high-impact Data Platform team responsible for building the infrastructure behind large-scale data products and analytics. You’ll design, develop, and maintain streaming and batch pipelines that process terabyte-scale datasets across modern cloud and data platforms. Your work will support analytics, data science, machine learning, CRM, experimentation, and emerging GenAI initiatives. You’ll combine strong software engineering practices with hands-on data engineering expertise to build reliable, scalable solutions. The position offers broad technical exposure across Java, Python, AWS, distributed systems, and modern data technologies. You’ll also collaborate with product, analytics, data science, and engineering teams across a global organization while taking meaningful ownership of end-to-end projects.

Design, build, and maintain robust ETL/ELT pipelines processing terabyte-scale data across Snowflake, BigQuery, Hive, and other data platforms. Develop scalable, reusable data models and curated datasets for analytics, data science, CRM, machine learning, and other internal data consumers. Own the full lifecycle of data pipelines, including defining SLAs, implementing performance measurements, monitoring, and anomaly detection. Ensure enterprise-level data integrity, validation, documentation, and governance. Develop production-quality Java and Python applications across data ingestion, event processing, REST services, and internal tooling. Build and maintain streaming and batch processing systems using technologies such as Flink, Spark, and Kafka. Take ownership of software engineering activities including architecture, implementation, QA, maintenance, and frequent production releases through CI/CD practices. Operate and improve cloud infrastructure on AWS while working with Linux, Gradle, and related engineering tools. Take ownership of complex projects independently and continuously improve existing systems and solutions. Contribute to code quality through technical design discussions, code reviews, and engineering best practices. Collaborate with Product, Design, Analytics, and Data Science teams to define requirements and deliver high-quality solutions. Communicate complex technical and data challenges clearly to both technical and business stakeholders. Work effectively with engineering teams located across different regions in a fast-paced, collaborative environment. Requirements 4–5+ years of professional experience in data engineering or software development within a commercial environment. Strong hands-on proficiency in both Java and Python for production systems. Proven experience designing and implementing complex ETL/ELT processes from concept through production. Strong SQL skills and experience exploring large, complex datasets. Experience with big data technologies such as Hadoop, Hive, BigQuery, and Snowflake, including terabyte-scale or larger datasets. Solid understanding of data structures, algorithms, and object-oriented design. Professional experience developing REST services and working with event queue systems. Familiarity with Linux and cloud infrastructure design, preferably on AWS or equivalent cloud platforms. Experience with system performance, optimization, and tuning, with an understanding of how architecture influences scalability. Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience. Strong analytical abilities and a commitment to writing clean, correct, efficient, and maintainable code. Organized, detail-oriented, self-directed, and comfortable taking ownership of projects with a strong sense of urgency. Ability to break down complex problems into practical, scalable solutions. Strong interpersonal and communication skills, combined with curiosity and enthusiasm for solving challenging technical problems. Willingness and ability to learn and adopt new technologies as the technical environment evolves. Experience with stream processing frameworks such as Flink or Spark Streaming is an asset. Experience with workflow and task orchestration tools such as Apache Airflow or similar platforms is an asset. Familiarity with technologies such as GraphQL, React, HTML5, JavaScript, CSS, Postgres, Gradle, or BERT is an asset. Experience designing infrastructure for large-scale data processing, including Hive, Snowflake, and NoSQL databases, is an asset. Experience with data governance practices and tooling is an asset. Exposure to or interest in machine learning, data science, or GenAI is an asset. Experience developing scalable systems for high-volume, low-latency workloads is an asset. Benefits Competitive compensation package, including base salary and annual bonus. Flexible, remote-friendly working model with the option to work on-site in select locations. Flexible scheduling designed to support work-life balance. Annual matching for qualifying charitable donations. Tuition assistance for eligible educational and professional development programs. Annual lifestyle benefit that can be used for travel, wellness, or other eligible personal expenses. Employee travel discounts and additional travel-related perks. Employee assistance program with resources and support for life’s challenges. Health benefits with competitive coverage and premiums. Generous employee referral program. Opportunities to work with large-scale data, AI, machine learning, cloud, and analytics technologies. Exposure to complex technical projects and collaboration with experienced engineering teams worldwide. An inclusive and collaborative environment that emphasizes learning, ownership, experimentation, and continuous improvement.

laptop-ats-crawler v3