Description de l'emploi

About Kinaxis

Kinaxis is a pioneering leader in supply chain orchestration, dedicated to providing innovative AI-infused platforms that enable complete end-to-end supply chain visibility. Founded in 1984 with a team of three engineers, Kinaxis has expanded globally with over 2000 employees, multiple international offices, and an award-winning headquarters in Ottawa. The company values a strong culture, technological innovation, and customer-centric approaches while maintaining a light-hearted workplace environment.

Our platform is trusted by leading global brands across 100+ countries and 40,000 users to deliver agility and predictability in today’s volatile markets.

Team and Role Overview

The Data & Analytics division at Kinaxis is focused on transforming the enterprise into a data-driven organization by establishing scalable, reliable, and modern data infrastructure that supports analytics, AI, and operational decision-making. The Data & Observability Platform team provides foundational technologies such as data ingestion frameworks, Databricks and dbt enablement, CI/CD processes, observability, and legacy platform modernization.

This managerial role entails overseeing this team, driving improvements in engineering speed, reducing delivery obstacles, and standardizing platform patterns to enable other teams to build and operate high-quality data products efficiently.

Key Responsibilities

Lead and mentor a team of data platform and observability engineers, fostering a culture centered on reliability, rapid delivery, automation, and continuous enhancement.
Define clear team goals aligned with broader Data & Analytics and Cloud Services strategies, coaching members on engineering best practices and stakeholder collaboration.
Develop and enhance reusable data ingestion frameworks and platform capabilities supporting analytics, AI, and customer-facing data products.
Manage Databricks and dbt environments, standardize deployment workflows, testing procedures, and operational methods.
Establish scalable standards for permissions, logging, monitoring, automation, and security compliance working in conjunction with Data Architecture and cloud/security teams.
Lead data and cloud observability initiatives including monitoring, alerting, telemetry, incident response, and operational dashboard development.
Drive legacy platform modernization efforts by planning and executing migration from tools like Informatica, Snowflake, Airflow, Postgres, Grafana, and Power BI Dataflows to contemporary, cloud-native solutions.
Collaborate closely with stakeholders across analytics, AI, SRE, cloud platform engineering, and business units to prioritize development and communicate progress clearly.
Improve developer experience by automating CI/CD pipelines, reducing manual intervention, and promoting infrastructure-as-code standards.

Technologies Utilized

Cloud Platforms: Google Cloud Platform, Microsoft Azure
Data Ecosystem: Databricks, dbt, BigQuery, Snowflake, PostgreSQL, SQL Server
Programming: Python, SQL
Orchestration and CI/CD: GitHub Actions, Airflow
Observability: Datadog, Grafana, Logstash, telemetry tools
Infrastructure: Terraform, Ansible
Development Tools: Visual Studio Code, Git, Bitbucket, Jira, Confluence

Candidate Profile and Qualifications

Bachelor’s degree in Computer Science, Engineering, Information Systems, or closely related discipline; Master's preferred.
Minimum 5 years of relevant experience in data engineering, platform, software, or cloud engineering roles.
At least 3 years in a leadership or management position within a fast-paced technical environment.
Proven expertise with modern cloud data platforms including Databricks, dbt, GCP, BigQuery, Snowflake, or similar technologies.
In-depth knowledge of data ingestion, modeling, orchestration, CI/CD, data quality, and operational management.
Experience designing reusable engineering frameworks and platform enablement capabilities.
Strong familiarity with observability practices such as logging, monitoring, alerting, incident handling, and system reliability.
Track record in migrating and modernizing legacy data platforms and tools.
Solid foundation in software engineering methodologies including version control, automated testing, and deployment automation.
Analytical communication skills to articulate technical decisions and influence leadership and cross-functional teams.
Experience with SaaS, enterprise software, or cloud-native industries advantageous.
Knowledge of FinOps and cloud cost management is beneficial.
Preferred hands-on skills: Python, SQL, dbt, Databricks, GCP.

Success Metrics

Acceleration of data source onboarding using approved ingestion frameworks.
Standardization and simplification of Databricks, dbt, and deployment approaches.
Timely retirement and migration from legacy platforms.
Enhanced data and cloud observability improving reliability and incident resolution.
Reduced operational friction in delivery pipelines and platform dependencies.
Consistent implementation of architectural standards without compromising speed.
Faster and more dependable delivery of analytics, AI, data products, and integration features.

Additional Information

Kinaxis deeply values diversity, equity, and inclusion, sustaining an inclusive hiring process with accommodations upon request for candidates with disabilities or specific needs. The organization prioritizes social responsibility, sustainability, and community engagement, emphasizing a positive impact through its operations and workforce culture.

Perks include flexible vacation and company-wide days off, options for flexible working, wellness programs, mentorship and training opportunities, recognition initiatives, and hackathons.

Applicants may request accommodations to ensure equity throughout recruitment.

Manager, Data Engineering