Senior Data Engineering

Plusgrade•Montreal, QC

30d

About The Position

We are seeking a Senior Data Engineer to play a key role in the evolution of our data platform. As part of our Data Engineering team, you will design and implement scalable, reliable, and high-performing data pipelines and platforms that support analytics, AI/ML, and customer-facing applications. In this role, youâll act as a technical leader: mentoring intermediate engineers, guiding solution design, and ensuring best practices are applied. Youâll work closely with a Staff Engineer and Data Architect, collaborating on architecture decisions while owning complex implementation and delivery.

Requirements

Bachelorâs or Masterâs degree in Computer Science, Engineering, or related field
5â7+ years of professional experience in data engineering, with proven success delivering large-scale data solutions
Advanced proficiency with Python, SQL, and at least one additional language (e.g., Java or Scala)
Deep expertise in SQL and NoSQL databases, data modeling, and query optimization
Strong hands-on experience with Snowflake and modern data warehouse concepts (data lakes, lakehouse, streaming)
Expertise with orchestration frameworks (Airflow, Dagster) and CI/CD for data pipelines
Solid understanding of real-time/streaming architectures (Kafka, Kinesis)
Proven track record of implementing data quality, observability, and monitoring frameworks
Strong understanding of AWS data ecosystem (S3, Glue, Lambda, EMR, ECS) or equivalent cloud services (Azure, GCP)
Experience with infrastructure as code (Terraform, CloudFormation)
Strong grounding in software engineering practices: testing, code reviews, version control, and design patterns
Excellent communication and collaboration skills, with the ability to influence technical decisions and guide cross-functional stakeholders
A customer-first mindset, focused on driving value and efficiency through robust and effective data products.

Nice To Haves

Experience with data governance, cataloging, and lineage tools (e.g., Collibra, Alation, Atlan)
Exposure to machine learning feature pipelines or MLOps workflows
Familiarity with DataOps practices and advanced monitoring/alerting platforms
Knowledge of RESTful API development and integration
Hands-on experience with Docker, Kubernetes, and containerized data workloads
Familiarity with BI and visualization platforms (Tableau, Looker)
Background in high-throughput or low-latency systems

Responsibilities

Lead the design, development and implementation of scalable, resilient, and cost-efficient data pipelines across batch and streaming systems
Partner with Staff Engineers and Data Architects to translate high-level architecture into actionable engineering solutions.
Drive adoption of best practices for data modeling, pipeline design, code quality, and testing
Implement and scale automated monitoring, alerting, and data quality frameworks for mission-critical pipelines
Own complex, high-impact data engineering projects end-to-end, from design through deployment and monitoring
Guide the team in designing event-driven, real-time, and microservices-oriented data flows
Optimize existing data infrastructure for performance, scalability, and cost efficiency in the cloud
Mentor and coach intermediate engineers, conducting reviews and providing feedback to grow team capability
Collaborate with analysts, data scientists, and product teams to co-design data products that power advanced analytics and ML models
Contribute to the evolution of data governance, security, and compliance practices
Evaluate and introduce new tools, frameworks, and techniques to keep our stack modern and efficient
Champion a data-first culture, ensuring stakeholders can trust and act on the data we provide