Manager, Data Engineering

Kinaxis

Engineering Manager for Data & Observability Platform at Kinaxis. Leading a team to improve engineering velocity and data capabilities in a cloud environment.

Posted 7/30/2026full-time🇨🇦 CanadaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading data platform engineering teams, focusing on modern cloud technologies such as Databricks, dbt, and GCP. Capable of building reusable frameworks and ensuring operational reliability through observability practices and CI/CD automation.

Highest-signal resume keywords

Data EngineeringPlatform EngineeringCloud EngineeringObservability PracticesTeam Leadership

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Hard Skills

Data IngestionData ModelingCI/CDAutomated TestingDeployment AutomationVersion ControlData QualityProduction OperationsTelemetryIncident Response

Soft Skills

Strong CommunicationCollaborationCoachingStakeholder PartnershipKnowledge Sharing

Tools & Technologies

DatabricksDbtGCPBigQuerySnowflakeSaaSCloud-Native EnvironmentsETL ToolsOperational DashboardsMonitoring

Industry Keywords

Data & AnalyticsCloud ServicesEnterprise SoftwareFinOpsData Products

Tech Stack

Tools & technologies

BigQueryCloudETLGoogle Cloud PlatformPythonSQL

About the role

Key responsibilities & impact

Lead, mentor, and manage a team of data platform engineers, and observability engineers
Build a high-performing engineering culture focused on reliability, delivery speed, automation, and continuous improvement
Define team objectives, delivery priorities, and measurable outcomes aligned with Data & Analytics and Cloud Services goals
Coach team members on engineering practices, operational ownership, platform thinking, and stakeholder partnership
Partner with other Data & Analytics leaders to ensure platform work is aligned to business and product priorities
Foster collaboration, knowledge sharing, and strong engineering discipline across the Data & Analytics organization
Own and evolve reusable ingestion frameworks, templates, and patterns for onboarding data into the modern data platform
Build and operate platform capabilities that support business analytics, AI enablement, product analytics, customer-facing data products, and integrations
Own Databricks and dbt platform enablement patterns, including environment standards, deployment workflows, testing approaches, and operational practices
Establish scalable patterns for service accounts, permissions, secrets, logging, monitoring, and deployment automation
Partner with Data Architecture to ensure platform patterns align with enterprise standards, security expectations, and long-term architectural direction
Enable other teams to ingest and operate data safely using approved frameworks and standards
Lead the delivery and ongoing operation of data observability and cloud observability platform capabilities for the Data & Analytics and the broader Cloud Services organization
Build and operate telemetry, monitoring, alerting, and reliability patterns for data pipelines, platform services, and cloud-facing workloads
Support observability needs for all data products and cloud infrastructure hosting Kinaxis’ flagship Maestro offering
Establish standards for pipeline health, data freshness, failure handling, operational dashboards, and incident response
Lead modernization of legacy data platforms, pipelines, and operational tooling into target-state GCP, Databricks, dbt, and cloud-native patterns
Improve developer experience for Data & Analytics teams through reusable frameworks, CI/CD automation, testing patterns, documentation, and self-service capabilities
Act as the primary platform partner for Analytics & AI Enablement, Data Products & Integrations, Data Architecture, SRE, and Cloud Platform Engineering
Communicate progress, risks, and modernization outcomes clearly to leadership

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field
A Master’s degree is a plus
5+ years of experience in data engineering, platform engineering, software engineering, cloud engineering, or related roles
3+ years of experience leading or managing technical teams in a fast-paced technology environment
Strong experience with modern cloud data platforms, preferably including Databricks, dbt, GCP, BigQuery, Snowflake, or similar technologies
Strong understanding of data ingestion, data modeling, orchestration, CI/CD, data quality, and production operations
Experience building reusable engineering frameworks, platform patterns, and developer enablement capabilities
Strong understanding of observability practices, including monitoring, alerting, logging, telemetry, incident response, and operational reliability
Experience modernizing or migrating legacy data platforms, ETL tools, pipelines, or reporting infrastructure
Strong software engineering fundamentals, including version control, automated testing, deployment automation, and code review practices
Ability to partner effectively with architects, product teams, analytics teams, SRE, Cloud Platform Engineering, and business stakeholders
Strong communication skills with the ability to explain technical trade-offs, risks, and delivery options to leadership
Experience in SaaS, enterprise software, or cloud-native environments is preferred
Experience with FinOps and/or cloud cost data is an asset
Experience with Python, SQL, dbt, Databricks, and GCP is strongly preferred.

Benefits

Comp & perks

Flexible vacation and Kinaxis Days (company-wide days off)
Flexible work options
Physical and mental well-being programs
Regularly scheduled virtual fitness classes
Mentorship programs, training, and career development
Recognition programs and referral rewards
Hackathons