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We are assembling an A-team of highly skilled, autonomous, and visionary engineers, and we are seeking an exceptional Lead Full Stack Data Engineer to join our high-performing, co-located squads in Canada. This senior role is for a hands-on player/coach who not only masters the full spectrum of data engineering but also demonstrates exemplary leadership, strategic thinking, and an unwavering commitment to leveraging AI for transformative productivity. The ideal candidate will take ownership of complex data products and platforms, driving the design, development, and optimization of end-to-end data solutions from ingestion to advanced consumption. We are looking for a true AI-first thinker who can architect scalable systems, mentor emerging talent, profoundly understand the functional domains our work impacts, and significantly contribute to our data strategy and culture.
Job Responsibility:
Lead and Architect end-to-end data solutions
Drive Strategic Initiatives within small, co-located squads
Act as a Player/Coach
Design, Develop, and Optimize highly efficient and resilient data ingestion, processing, and transformation pipelines using advanced Python and PySpark techniques
Architect and Implement sophisticated data storage solutions leveraging a diverse set of big data technologies
Champion Data Modeling and Governance
Strategically Engage with data consumers, data scientists, and business stakeholders
Lead the Implementation of real-time data streaming and complex event-driven architectures
Enforce and Evolve Best Practices in data engineering and software development
Exhibit High Autonomy and Agency
Innovate with AI-Powered Development
Shape the Future of Our Data Stack
Expertly Troubleshoot and Resolve the most challenging technical issues
Requirements:
6+ years of progressive, hands-on experience as a Senior/Lead Data Engineer
Expert-level proficiency in Python
Deep expertise in developing highly optimized, scalable, and production-grade PySpark applications
Deep architectural understanding and extensive hands-on experience with the entire Apache Spark ecosystem (Spark Core, Spark SQL, Spark Streaming, Spark MLlib)
Advanced proficiency with Hive for enterprise data warehousing
Expert knowledge of distributed computing fundamentals, HDFS, and other components of the Hadoop ecosystem
Master-level proficiency in SQL, complex query optimization, and advanced data warehousing concepts
Extensive experience with various data storage formats (e.g., Parquet, ORC, Avro) and leading data lake solutions (e.g., Delta Lake, Iceberg)
Proven experience with enterprise-grade NoSQL databases (e.g., Cassandra, MongoDB, HBase)
Expert-level experience with Apache Kafka
Extensive experience with big data services on major cloud platforms (e.g., AWS EMR/Glue/Redshift/Kinesis, Azure Databricks/Data Factory/Synapse/Event Hubs, GCP Dataflow/Dataproc/BigQuery/Pub/Sub)
Demonstrated mastery and innovative application of AI coding tools (e.g., Claude Code, Codex, Antigravity)
Advanced understanding of software engineering principles, design patterns, data structures, algorithms, and performance engineering for distributed systems
Extensive experience with RESTful API design, development, and integration for data services
Strong expertise in containerization technologies (e.g., Docker, Kubernetes) and orchestration
Master-level proficiency with version control systems, especially Git
Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a relative experience is required