Full-Time :: Toronto, ON (Hybrid, 2 days/week) :: Data Engineer – Databricks, SQL, Python & ETL; Canadian Citizens / P.R

Data Engineer – Databricks, SQL, Python & ETL || Canadian Citizens / P.R

Full-Time Permanent

Toronto, ON (Hybrid, 2 days/week) 

Salary: $Market/CAD per annum + Benefits

Eligibility: Open to Canadian Citizens / P.R , and valid Work Permit holders

Role Summary
We are hiring a Senior Data Engineer with 7+ years of experience to design, build, and optimize scalable data platforms and pipelines in a cloud-first, consulting environment. You will lead data integration and migration initiatives, implement robust ELT/ETL patterns, and partner closely with business and technical stakeholders to deliver reliable, high‑performance solutions on Databricks and modern lakehouse architectures.

Key Responsibilities
- Design, develop, and maintain batch and streaming data pipelines using Databricks, SQL, and Python/PySpark
- Build and optimize ELT/ETL workflows for large, complex, and high‑volume datasets
- Implement data models and lakehouse patterns (Delta Lake/Delta tables), including partitioning, Z‑ordering, and schema evolution
- Lead and execute data migration and integration from on‑prem and legacy systems to cloud data platforms
- Tune and optimize Spark jobs, clusters, and queries for cost, performance, and reliability
- Orchestrate workflows using ADF, Airflow, and/or Databricks Workflows and Jobs
- Apply data quality, governance, lineage, and validation best practices; contribute to standards and reusable frameworks
- Collaborate with architects, analysts, and product teams to translate requirements into technical designs and delivery plans
- Implement CI/CD and DevOps practices (branching, code reviews, environment promotion, automated testing)
- Produce clear documentation and provide knowledge transfer; mentor junior engineers and contribute to delivery excellence

Required Experience and Skills
- 7+ years in Data Engineering, Data Integration, ETL/ELT, or similar data-focused roles
- Deep hands‑on experience with Databricks for data engineering (Jobs/Workflows, notebooks, clusters)
- Advanced SQL with proven work on large-scale datasets and complex transformations
- Strong Python and/or PySpark development skills; solid understanding of distributed data processing with Apache Spark
- Expertise in data modeling, dimensional design, and data warehousing concepts
- Demonstrated success building production-grade ELT/ETL pipelines and reusable components
- Hands‑on experience with at least one major cloud (Azure, AWS, or Google Cloud Platform) and related data services
- Experience with Delta Lake/Delta tables and modern lakehouse architecture
- Familiarity with orchestration tools (e.g., Azure Data Factory, Airflow) and Databricks Workflows/Jobs
- Proficiency with Git and CI/CD practices; comfort working within modern DevOps processes
- Strong grasp of data quality, governance, lineage, and validation principles
- Excellent troubleshooting, performance tuning, and problem-solving abilities
- Clear, proactive communicator able to work with both technical and business stakeholders in a consulting/client-facing setting

Preferred Qualifications
- Delivery experience on large-scale enterprise data engineering programs
- Background in financial services, banking, insurance, or consulting
- Hands-on experience migrating from legacy platforms to cloud-based lakehouse architectures
- Exposure to Databricks Unity Catalog and performance optimization features
- Experience working within Agile/Scrum teams
- Bachelor’s degree in Computer Science, Engineering, Information Systems, or equivalent experience

Success Indicators
- Reliable, well-documented pipelines with strong data quality and lineage
- Measurable performance and cost improvements across Spark/Databricks workloads
- On-time delivery of migration milestones and platform enhancements
- Positive stakeholder feedback and effective collaboration across teams