Lead Data Engineer

Lead Data Engineer Location – Downtown Toronto (hybrid - minimum 3 days in a week) Duration: Long term Role: - Lead the development and maintenance of scalable, reliable data pipelines and data processing frameworks supporting a variety of business and product use cases. - Ensure data quality, integrity, and readiness by establishing and maintaining standards, validation processes, and monitoring frameworks. - Collaborate with cross-functional teams (Data Science, Product, Analytics, Infrastructure, and Engineering) to deliver end-to-end data solutions. - Identify and implement ETL/ELT processes, focusing on code robustness, automation, efficiency, and operational excellence. - Integrate emerging technologies to enhance data engineering capabilities and support evolving business needs. - Ensure timely, high-quality delivery while balancing multiple priorities. - Act as a subject matter expert on data modeling, pipeline optimization, large-scale data processing, and best practices. - Ensure compliance with internal policies and external data regulations, promoting secure and responsible data usage across the team. All About You - Extensive experience as a Data Engineer or in a similar role, with deep expertise in data engineering principles, data modeling, and pipeline development. - Experience working with big data and distributed systems (e.g. Spark, Hadoop, cloud-native big data services). - Strong working experience in Databricks, Hadoop-pySpark and related tools and technologies like, Apache Airflow, NiFi along with open formats like Delta and Iceberg - Strong SQL knowledge translating into analytical skills required for data analysis and defect management process - Strong understanding of data quality frameworks, validation methods, and monitoring tools - Familiarity with Agile methodologies and modern DevOps practices for data engineering - Working with CI/CD pipelines and modern source control practices - Strong communication skills - both verbal and written and strong relationship, collaboration skills and organizational skills - Ability to be high-energy, detail-oriented, proactive and able to function under pressure in an independent environment along with a high degree of initiative and self-motivation to drive results - Ability to quickly learn and implement new technologies, and perform POC to explore best solution for the problem statement - Flexibility to work as a member of a matrix based diverse and geographically distributed project teams.

Lead the development and maintenance of scalable data pipelines and processing frameworks while ensuring data quality and integrity. Collaborate with cross-functional teams to implement robust ETL/ELT processes and integrate emerging technologies.

Extensive experience in data engineering with deep expertise in data modeling, big data, and distributed systems. Proficiency in Databricks, PySpark, Airflow, and SQL is required, along with familiarity with Agile and DevOps practices.

Identified from the job description. Confirm important requirements above.

• Data Engineering
• Data Modeling
• ETL/ELT
• Databricks
• Hadoop
• PySpark
• Apache Airflow
• NiFi
• Delta Lake
• Iceberg
• SQL
• Big Data
• Distributed Systems
• Agile
• DevOps
• CI/CD

• Data & Analytics
• Technology
• Software
• Engineering
• Consulting

Minimum experience

5+ years

Apply by

Aug 21, 2026

Posting language

English

Working hours

40 hours per week

Office presence

3 days per week

Seniority

Mid-Senior level

Application method

Direct apply is available