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)