Lead Data Engineer​/Data Platform Lead

121 Bloor st E, Toronto, Canada (onsite 5days)

• Role 2 is a more senior and strategic Lead Data Engineer / Data Platform Lead role. In addition to hands‑on engineering, it emphasizes technical leadership, enterprise architecture, analytics enablement, stakeholder management, innovation, and long‑term platform strategy . It also introduces preferred experience in GenAI/LLM-enabled data platforms , making it broader in scope than the first role.

• Strong proficiency in Python, including Pandas, Num Py, PySpark, with hands on experience using Impala.
• Proven experience working on Hadoop based platforms, performing large scale data extraction, transformation, and processing.
• Strong SQL skills and experience working with both relational and distributed data stores.
• Experience with enterprise data platforms and business intelligence ecosystems.
• Hands on experience with ETL / ELT and data integration tools, such as Apache Airflow, Apache NiFi, Azure Data Factory.
• Experience in data modelling, querying, data mining, and reporting over large volumes of granular data.
• Exposure to machine learning concepts and analytical techniques used in advanced data solutions and Feature calculations and Model serving is a big plus.
• 8+ years of experience in data engineering, big data analytics, or enterprise data platforms, including 2+ years in a lead or technical leadership role.
• Experience working with cloud based data platforms (Azure/AWS, Databricks/Snowflake), including data lakes, distributed compute, and storage services.
• Experience implementing CI/CD pipelines and Dev Ops practices for data engineering workflows.

• Experience enabling GenAI/AI products through scalable, reliable data ingestion and transformation pipelines (batch and streaming).
• Exposure to unstructured and semi-structured data processing (documents/logs/text) and building curated datasets for downstream consumption.
• Strong understanding of data governance, privacy, and security requirements when using enterprise data with AI (PII handling, access control, auditability).
• Familiarity with operationalizing AI data workflows (monitoring, data quality checks, reproducibility, and cost-aware scaling in cloud environments).