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Lead Data Platform Engineer

Job in Toronto, Ontario, C6A, Canada
Listing for: High Tech Genesis Inc.
Full Time position
Listed on 2026-08-01
Job specializations:
  • IT/Tech
    Data Engineering, Data Warehousing, Data Science Manager
Salary/Wage Range or Industry Benchmark: 140000 - 190000 CAD Yearly CAD 140000.00 190000.00 YEAR
Job Description & How to Apply Below

Overview

We are seeking a Lead Data Platform Engineer to design, build, and optimize scalable data platforms and pipelines that support enterprise analytics and data-driven solutions. This role combines hands-on engineering with technical leadership, driving best practices in data architecture, platform performance, and governance while mentoring engineering teams.

Key Responsibilities
  • Design, develop, and maintain scalable data pipelines and ETL/ELT workflows.
  • Build and optimize data platforms using Hadoop, Databricks, and cloud-based technologies.
  • Integrate structured and semi-structured data into reliable, high-quality data solutions.
  • Partner with cross-functional teams to translate business and analytics requirements into scalable engineering solutions.
  • Lead technical design discussions and promote best practices in data modeling, performance optimization, and governance.
  • Mentor data engineers and contribute to engineering standards, architecture, and platform scalability.
  • Support innovation through proof-of-concepts, automation, and continuous platform improvements.
  • 8+ years of experience in data engineering, including 2+ years in a technical leadership role.
  • Strong Python skills (Pandas, Num Py, PySpark) and experience with Impala.
  • Hands-on experience with Hadoop, Databricks, and large-scale data processing.
  • Advanced SQL and experience with relational and distributed databases.
  • Experience with cloud platforms such as Azure or AWS, including Databricks or Snowflake.
  • Strong knowledge of ETL/ELT tools such as Apache Airflow, Apache NiFi, or Azure Data Factory.
  • Experience with CI/CD, Dev Ops practices, and enterprise data platforms.
  • Understanding of data modeling, governance, and performance optimization.
Nice to Have
  • Experience supporting AI/GenAI solutions through scalable data pipelines.
  • Knowledge of machine learning workflows, feature engineering, and model serving.
  • Experience processing unstructured data and implementing data governance, privacy, and security best practices.
  • Strong analytical and problem-solving skills with the ability to communicate effectively across technical and business teams.
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