Skip to content

Lead Data Platform Engineer

  • Hybrid
  • CA$80 - CA$85 per hour

Full Time

Current Vacancy

Job description

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.

Job requirements

  • 8+ years of experience in data engineering, including 2+ years in a technical leadership role.

  • Strong Python skills (Pandas, NumPy, 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, DevOps 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.

Hybrid
  • Toronto, Ontario, Canada
CA$80 - CA$85 per hour

or