• Develop and maintain metadata-driven data pipelines for ingestion, transformation, and delivery of data • Build and support ETL/ELT processes for both legacy and modern data sources • Implement data transformations to convert legacy formats (e.g., SFTP, flat files) into modern formats (e.g., XML, JSON, APIs) • Develop API integrations and data services supporting real-time and near real-time data exchange • Support implementation of data quality checks, validation rules, and monitoring processes • Collaborate with a Data Architect to implement data models, schemas, and integration patterns • Optimize pipelines and queries for performance, scalability, and reliability • Assist in documenting data flows, interfaces, and pipeline configurations • Support DevSecOps practices to ensure a standardized data environment, including implementing CI/CD pipelines, automated testing, and deployment • Work in cross-functional team with data scientists, data engineers, and business stakeholders

Role Description We are currently building a next-generation Data Fabric—a multi-tenant, high-performance platform built on Databricks that will serve as the substrate for all Kinaxis applications. We aren’t looking for an integrator to build pipelines; we need a Systems Architect to build the engine. - Architect the Fabric: Lead the technical vision for a Databricks-centric platform, focusing on Delta Sharing, Unity Catalog, and custom data orchestration. - Solve for "The Platform": Move beyond simple ingestion. You will design the "low-level" primitives—identity, compute quotas, and automated RBAC—that make the platform scalable for thousands of external users. - Bridge the Gap: Translate complex infrastructure constraints (Kubernetes, Azure/GCP, Databricks) into a seamless developer experience for our internal and external application teams. - Direct Impact: Your contributions will have direct impact on the Kinaxis platform. Qualifications - 10+ years of experience as a Systems Thinker. - Deep hands-on experience with Databricks, Spark, and Delta Lake. - Fluency in Kubernetes, containerization, and the networking/security layers of Azure or GCP. - Ability to debate long-term architectural strategy and engage in complex debugging sessions. - Experience in finding opportunities to accelerate the SDLC through innovative applications of AI. Requirements - Understanding of the difference between a solution and a scalable platform. - Ability to review AI-generated code for correctness, architectural fit, integration risk, and edge case support. Benefits - Flexible vacation and Kinaxis Days (company-wide day off on the last Friday of every month). - Flexible work options. - Physical and mental well-being programs. - Regularly scheduled virtual fitness classes. - Mentorship programs and training and career development. - Recognition programs and referral rewards. - Hackathons.

• Design, build, and maintain end‑to‑end data pipelines and integrations to support Medicine & Analytics use cases (batch and event‑driven patterns where relevant). • Develop, operate, and optimize integrations using SnapLogic and AWS services such as S3, AWS Lambda, and AWS Glue to ensure robust ingestion, transformation, and orchestration. • Implement and maintain analytics‑ready data models in Snowflake, ensuring performance, scalability, and cost‑efficient design. • Build transformation logic and analytics layers using dbt, including modular modeling, testing, documentation, and deployment best practices. • Contribute to and enforce data governance standards by leveraging tools such as Collibra, ensuring metadata quality, lineage, ownership, and consistent definitions. • Partner with Data Quality stakeholders to implement and monitor quality controls using Attaccama, including rules, profiling, exception handling, and remediation workflows. • Support data lifecycle processes and operationalization of data products using Innovator (as applicable in the ecosystem) to align delivery with platform and product standards. • Proactively identify opportunities to simplify architecture, automate repetitive work, and reduce operational effort (observability, alerting, self‑healing patterns). • Ensure all solutions follow security, privacy, and compliance expectations (e.g., regulated environment practices, audit readiness, access controls, data handling). • Collaborate closely with Product Owners, Data Scientists, Analysts, Architects, and business stakeholders to translate needs into reliable, reusable data assets. • Act as a role model for engineering excellence: version control, CI/CD, code reviews, documentation, and operational runbooks.

• Build, maintain, and run CI/CD pipelines and infrastructure-as-code for the Smile Digital Health platform and associated services. • Provision, configure, and operate cloud-based Spark clusters and distributed data processing environments, including hands-on work with orchestration tools such as Airflow, Databricks, or EMR. • Write, test, and maintain data pipelines on the same infrastructure you manage, from environment setup through to production monitoring. • Design and maintain scalable, secure infrastructure templates and deployment automation across AWS, Azure, GCP, or OCI environments. • Investigate and resolve data pipeline and integration issues, providing root-cause analysis and durable fixes. • Monitor running systems and pipelines, respond to incidents, tune performance, and manage cloud infrastructure costs. • Foster an Everything-as-Code culture and promote DataOps best practices across the team. • Assist developers and engineers with deployments and builds as needed.