Sr. Data Engineer - Toronto, ON (Hybrid)
Hands-on Sr. Data Engineer embedded with a Total Fund/Portfolio Management Technology team, building and optimizing large-scale Spark/PySpark data pipelines in Databricks on cloud (Parquet/Iceberg). Independent, low-supervision subcontractor role, 4 days onsite per week in Toronto.
- Compensation
- Not disclosed
- Location
- Toronto, Canada
- Work setup
- Hybrid
- Seniority
- Senior
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data engineering
- Posted
Skills
Must have
- Strong Python with PySpark
- Hands-on experience with Apache Spark (preferably in cloud environments)
- Experience working with large-scale data systems
- Familiarity with columnar formats (Parquet) and modern table formats (Iceberg)
- Experience with Databricks
- Prior consulting, advisory, or client-facing delivery experience
- Ability to operate effectively in ambiguous environments
- Proven independent delivery without close supervision
- Strong problem-solving and continuous improvement mindset
Nice to have
- AWS data services (Glue, Lake Formation)
- Workflow orchestration tools (Airflow)
- Experience with distributed data processing architectures
- Capital markets experience
Full description
Location: Toronto, ON (4 Days onsite/week)
Start date: ASAP
Project Description
We are seeking a hands-on Data Engineer to support the Total Fund Management Portfolio Management Technology team. This role focuses on building and optimizing large-scale data processing systems in a cloud-native environment. You are expected to independently design and deliver scalable data solutions with minimal oversight.
This is not a role for someone who requires detailed task breakdowns or constant direction. Once objectives and constraints are clear, you are expected to independently drive work to completion, proactively manage risks, and communicate progress effectively. This role is for subcontractors engagement.
Responsibilities
- Develop and optimize Spark-based workloads in cloud environments
- Work with large-scale datasets using modern storage formats.
- Collaborate with stakeholders to translate data requirements into robust engineering solutions
- Ensure high performance, reliability, and maintainability of data platforms
- Contribute to production readiness, including monitoring, documentation, and deployment
Skills - Must have
- Strong Python with PySpark
- Hands-on experience with Apache Spark (preferably in cloud environments)
- Experience working with large-scale data systems
- Familiarity with columnar formats (Parquet) and modern table formats (Iceberg)
- Experience with Databricks
- Prior consulting, advisory, or client-facing delivery experience
- Ability to operate effectively in ambiguous environments
- Proven independent delivery without close supervision
- Strong problem-solving and continuous improvement mindset
Nice to have
- AWS data services (Glue, Lake Formation)
- Workflow orchestration tools (Airflow)
- Experience with distributed data processing architectures
- Capital markets experience
Why This Role
- Opportunity to directly impact business growth by identifying and hiring the right IT talent for critical projects.
- Hands-on exposure to diverse technologies and stakeholders, strengthening recruitment and domain expertise.
Company Name: Pozent
- Pozent is a rapidly growing company focused on delivering next-generation digital, data, and AI-driven solutions to global clients and achieve measurable business outcomes.