Sr. Data Engineer - Toronto, ON (Hybrid)

Résumé du poste

Develop and optimize Spark-based workloads and large-scale data processing systems in a cloud-native environment. Collaborate with stakeholders to design scalable data solutions and ensure the reliability and maintainability of data platforms.

Détails du poste

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. Our team is geared towards innovation and thrives where talent meets opportunity. We work on Generative AI, Cloud, Data Engineering, Automation, and Full-Stack Development, delivering impactful projects Why Join Us Exciting Global Projects and career growth: Work directly with top clients and cutting-edge technologies providing a rapid learning curve, skill development, and clear career progression. Innovation Culture and supportive environment: You are not just joining a company — you're becoming part of a dynamic innovation ecosystem that empowers you to learn, explore, create, grow and lead.

Ce que vous ferez

Develop and optimize Spark-based workloads and large-scale data processing systems in a cloud-native environment. Collaborate with stakeholders to design scalable data solutions and ensure the reliability and maintainability of data platforms.

Exigences

Requires strong proficiency in Python, PySpark, and Databricks, with experience in columnar formats like Parquet and Iceberg. Candidates must demonstrate the ability to deliver complex projects independently in ambiguous environments.

Compétences indiquées

• Python · Souhaitée

Autres compétences pertinentes

Relevées dans la description du poste. Confirmez les exigences importantes ci-dessus.

• Python
• PySpark
• Apache Spark
• Databricks
• Parquet
• Iceberg
• AWS Glue
• AWS Lake Formation
• Airflow
• Distributed Data Processing
• Capital Markets
• Cloud-native Data Engineering

Domaines d’emploi

• Data & Analytics
• Technology
• Software
• Engineering
• Finance & Accounting