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Lorven technologies
Lead Data Platform Engineer - Downtown Toronto, ONLorven technologies • Toronto, ON, Canada
Lead Data Platform Engineer - Downtown Toronto, ON

Lead Data Platform Engineer - Downtown Toronto, ON

Lorven technologies • Toronto, ON, Canada
6 days ago
Job type
  • Full-time
  • Quick Apply
Job description

Our client seeks an Lead Data Platform Engineer for a 12 Months project in Downtown Toronto. Below is the detailed requirement

Job Title: Lead Data Platform Engineer

Work location : Downtown Toronto, ON

Duration: 12 Months

Job Description:

  • Bachelor's degree preferably in Computer Science, Information technology, Computer Engineering, or related IT discipline or equivalent experience with 12+ Minimum Experience
  • 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)
  • 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
  • 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).
  • Analytical & Business Acumen
  • Strong experience collecting, standardising, and summarising diverse datasets while identifying patterns, inconsistencies, and data quality issues.
  • Solid understanding of how analytics, metrics, and visualisation support business decision making.
  • Ability to comprehend complex operational systems and deliver scalable analytics and information products to a global user base.
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