Lead Data Engineer / Data Platform Lead

• 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.

• All About You
• Technical Skills & Experience
• • Strong proficiency in Python, including Pandas, NumPy, 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 DevOps practices for data engineering workflows.
• GenAI / LLM Skills (Preferred)
• • 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).