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Kinaxis

Manager, Data & Observability Platform

Kinaxis

Engineering Manager overseeing the Data & Observability Platform team's operations and capabilities at Kinaxis. Driving modernization and collaboration while enhancing data solutions.

Posted 7/31/2026full-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. Proven ability to establish engineering best practices, drive modernization efforts, and enhance operational reliability through observability and automation.

Highest-signal resume keywords
Data EngineeringPlatform EngineeringCloud EngineeringObservability PracticesTeam Leadership

ATS Keywords

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Hard Skills
DatabricksDbtGCPBigQuerySnowflakeData IngestionData ModelingCI/CDAutomated TestingDeployment Automation
Soft Skills
Strong CommunicationTeam CollaborationMentoring
Tools & Technologies
InformaticaAirflowPostgresGrafanaPower BI
Industry Keywords
SaaSEnterprise SoftwareCloud-NativeFinOps

Tech Stack

Tools & technologies
AirflowBigQueryCloudETLGoogle Cloud PlatformGrafanaInformaticaPostgresPythonSQL

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.
  • Lead the migration and retirement strategy for legacy technologies such as Informatica, Snowflake, Airflow, Postgres, Grafana, and Power BI Dataflows where applicable.
  • Ensure migration work is delivered incrementally, safely, and with clear business continuity plans.
  • Reduce technology fragmentation by creating repeatable patterns and minimizing one-off solutions.
  • Partner with consuming teams to prioritize modernization work based on risk, business value, operational burden, and renewal timelines.
  • Improve developer experience for Data & Analytics teams through reusable frameworks, CI/CD automation, testing patterns, documentation, and self-service capabilities.
  • Reduce dependency on manual cloud changes and external platform approvals by partnering with SRE and Cloud Platform Engineering on approved automation patterns.
  • Establish practical standards for analytics-as-code, infrastructure-as-code, testing, deployment, and operational readiness.
  • Identify bottlenecks in delivery flow and implement platform capabilities that reduce cycle time and rework.
  • Promote a 'thin vertical slice first, harden and scale after' delivery mindset where appropriate.
  • Act as the primary platform partner for Analytics & AI Enablement, Data Products & Integrations, Data Architecture, SRE, and Cloud Platform Engineering.
  • Translate platform needs, risks, and dependencies into clear plans and trade-offs for technical and non-technical stakeholders.
  • Communicate progress, risks, and modernization outcomes clearly to leadership.
  • Support architecture review processes by ensuring new patterns are reviewed early and implemented consistently.
  • Build strong relationships with teams that depend on the data platform for business analytics, customer-facing insights, integrations, FinOps, observability, and AI enablement.

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