Senior Data Engineer

What You'll Be Doing

Technical Leadership
• Lead the architecture, design, and implementation of enterprise-scale data platforms from project inception through production deployment.
• Own technical delivery across multiple client engagements while ensuring high-quality engineering standards.
• Define solution architecture, technical roadmaps, and implementation strategies aligned with client business goals. 
• Conduct architecture reviews, code reviews, and establish engineering best practices across project teams.
• Mentor and coach Data Engineers while fostering technical excellence and continuous learning.
• Serve as the primary technical leader for complex engineering initiatives and critical project decisions.
Client Partnership
• Partner directly with Fortune 500 clients to understand business requirements and translate them into scalable technical solutions.
• Lead discovery workshops, architecture sessions, and technical planning meetings with both business and engineering stakeholders.
• Present solution designs, delivery plans, and architectural recommendations to technical leadership and executive audiences.
• Build trusted relationships with client teams while providing technical guidance throughout project execution.
• Support pre-sales activities by contributing technical expertise, solution estimates, and implementation approaches when required.
Data Engineering & Platform Development
• Design, develop, and optimize enterprise-grade data pipelines using the Databricks Unified Analytics Platform.
• Build scalable ETL and ELT frameworks capable of processing large-scale structured and unstructured datasets.
• Design and implement Lakehouse architectures using Delta Lake and Medallion design patterns.
• Develop high-performance Spark applications for batch and real-time data processing.
• Integrate data from enterprise applications, APIs, streaming platforms, and cloud storage solutions.
• Ensure data quality, integrity, and reliability through automated validation, testing, and monitoring.
Cloud & DevOps
• Architect cloud-native data platforms across AWS, Azure, or Google Cloud Platform.
• Implement Infrastructure-as-Code using Terraform or similar technologies.
• Build and maintain CI/CD pipelines supporting automated testing and deployment.
• Optimize cloud infrastructure for scalability, reliability, security, and cost efficiency.
• Monitor platform performance and proactively resolve operational issues.
Data Governance & Security
• Implement enterprise data governance frameworks and security best practices.
• Configure Unity Catalog, metadata management, lineage, and role-based access controls.
• Ensure compliance with organizational security standards and regulatory requirements.
• Promote data observability and operational excellence across production environments
Cross-Functional Collaboration
• Partner closely with Product Managers, Data Scientists, Analytics Engineers, Machine Learning Engineers, and Software Engineers to deliver high-impact data products.
• Enable AI and machine learning initiatives through scalable feature engineering pipelines and production-ready datasets.
• Contribute reusable frameworks, accelerators, and engineering standards that improve delivery across client engagements.

What You'll Bring

• Bachelor's or Master's degree in Computer Science, Data Engineering, Software Engineering, or a related technical discipline.
• 8+ years of experience designing and building enterprise-scale data platforms.
• 5+ years of hands-on experience with Databricks and Apache Spark.
• Proven experience leading enterprise data engineering projects from architecture through production delivery.
• Strong expertise in Python, SQL, and Spark for large-scale data processing.
• Deep understanding of Delta Lake, Lakehouse architecture, and modern data platform design.
• Experience working with AWS, Azure, or Google Cloud Platform.
• Strong knowledge of ETL/ELT frameworks, distributed computing, and data modeling.
• Experience implementing CI/CD pipelines and Infrastructure-as-Code.
• Strong understanding of data governance, security, metadata management, and data quality practices.
• Experience optimizing distributed data processing workloads for performance and cost.
• Excellent communication and stakeholder management skills with experience working directly with enterprise clients. 
• Demonstrated ability to mentor engineers and lead technical initiatives

Nice to Have

• Databricks Certified Professional Data Engineer certification.
• Experience with Delta Live Tables, MLflow, Unity Catalog, and Databricks SQL.
• Experience with Kafka, Kinesis, Event Hubs, or other streaming technologies.
• Hands-on experience with Snowflake, dbt, Airflow, or modern data orchestration tools.
• Experience with Kubernetes, Docker, and Terraform. 
• Knowledge of AI/ML data platforms, Feature Stores, or Retrieval-Augmented Generation (RAG) architectures.
• Previous consulting or professional services experience delivering solutions for enterprise clients.
• Experience within retail, e-commerce, financial services, logistics, healthcare, or ad-tech environments.

Salary Range

• $140,000 – $180,000 (CAD)