Posted 4 months ago
Lead Data Engineer
@ Princeton IT Services, Inc
Toronto, Ontario
SIGNALS
Requirements summary:
- 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
Source and dates
Source type not verified · apify-linkedin
- Employer posting date
- 2026-07-16
- First seen by our source
- 2026-11-11
- Source record updated
- 2026-11-11
These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.
Princeton IT Services, Inc
| Field | Value |
|---|---|
| Open jobs on Jobsy | 8 |
Job description
Job Type: Lead Data Engineer
Location: Toronto, Canada (onsite 5days)
Summary: This is best described as a Lead Data Engineer responsible for building and operating scalable, high-quality data pipelines. It is more execution-focused with emphasis on engineering excellence, ETL/ELT, data quality, and operational reliability.
Role
Location: Toronto, Canada (onsite 5days)
Summary: This is best described as a Lead Data Engineer responsible for building and operating scalable, high-quality data pipelines. It is more execution-focused with emphasis on engineering excellence, ETL/ELT, data quality, and operational reliability.
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.
- 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
Journal
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