Location

• Ottawa and Toronto, Canada - Hybrid (Preferred)
• Other Canadian locations - Remote

About the team

The Data & Analytics organization drives Kinaxis’ transformation into a data-driven organization by building trusted, scalable, and modern data capabilities that power analytics, AI enablement, customer-facing data products, cloud intelligence, and operational decision-making.

The Data & Observability Platform team is responsible for the shared technical foundations that enable the broader Data & Analytics organization to deliver quickly and reliably. This includes ingestion frameworks, Databricks and dbt platform enablement, CI/CD and deployment patterns, data and cloud observability, modernization of legacy platforms, and reusable standards that allow teams to safely build and operate data solutions at scale.

This team partners closely with Data Architecture, Analytics & AI Enablement, Data Products & Integrations, FinOps, SRE, Cloud Platform Engineering, and business stakeholders across Kinaxis.

Vacancy Status

This is an existing job vacancy

What you will do

We are seeking an experienced and hands-on engineering manager to lead the Data & Observability Platform team. This role will be responsible for building and operating the shared platform capabilities, frameworks, and observability foundations that support Kinaxis’ modern data ecosystem.

You will lead a team focused on improving engineering velocity, reducing delivery friction, enabling consistent platform patterns, and modernizing legacy data technologies. Your team will provide the foundation that allows other Data & Analytics teams to ingest, transform, monitor, and operate data products and analytics solutions reliably.

Success in this role will require strong technical leadership, operational discipline, stakeholder partnership, and the ability to balance platform maturity with pragmatic delivery. You will help the organization move faster by creating reusable frameworks, clear standards, and reliable platform capabilities.

• 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.

Technologies we use

• Cloud & Platform: Google Cloud Platform, Microsoft Azure, Databricks
• Data Engineering & Modeling: Python, SQL, dbt
• Data Stores: BigQuery, Snowflake, Postgres, SQL Server, Databricks
• BI & Analytics Consumers: Power BI, Looker
• Orchestration & CI/CD: GitHub Actions, Airflow, CI/CD pipelines
• Observability: Datadog, Grafana, Logstash, cloud telemetry platforms
• Infrastructure Automation: Terraform, Ansible
• Development Tools: Visual Studio Code, Git, Bitbucket/Stash, Jira, Confluence
• Integration Development: GCP-native Python-based integration pattern

What we are looking for

• Bachelors degree in Computer Science, Engineering, Information Systems, or a related field. A Masters 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.

• Data & Analytics teams can onboard new data sources faster using approved ingestion frameworks.
• Databricks, dbt, and deployment patterns become more standardized and easier to use.
• Legacy platform retirement progresses against agreed timelines.
• Data and cloud observability capabilities improve operational reliability.
• Platform incidents are easier to detect, diagnose, and resolve.
• Delivery teams experience less friction from permissions, environments, CI/CD, and platform dependencies.
• Architecture standards are implemented consistently without slowing delivery.
• Stakeholders see faster, more reliable delivery of analytics, AI, data product, and integration capabilities.

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