• Design, develop, and maintain dbt data models that support our healthcare analytics products. • Integrate and transform customer data to conform to our data specifications and pipelines. • Design and execute initiatives that improve data platform and pipeline automation and resilience. • Participate in a rotation of engineers that diagnose, triage, and solve production data issues. • Apply industry standards and best practices to data testing, observability, and platform stability.

• Build data pipelines for coaching and user analytics • Create data systems that power product features • Establish data infrastructure and architecture • Process and analyze AI/LLM outputs • Support business operations and analytics • Create accessible analytics infrastructure • Work closely with product on instrumentation and data collection

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description Business Intelligence at Jamf powers data-driven decision-making across the organization. As a Data Platform Engineer II, you’ll be responsible not just for building & transforming data, but for owning critical data infrastructure: from ingestion and storage, to governance, quality, and consumption by analytics/ML tools. You will partner with analysts, data scientists, product owners and engineers to ensure that Jamf’s data assets are reliable, high-performance, secure, and scalable. For those candidates who live near a Jamf office, you may be expected to work periodically in-office or collaborative work location with other Jamf employees in your area for certain events or moments that matter. What you can expect to do in this role: - Design, build, maintain, and improve the data platform infrastructure (Snowflake environments, airflow workflows, orchestration, CI/CD pipelines for dbt / transformations) - Develop and maintain Terraform (or equivalent IaC) definitions for provisioning data infrastructure (compute, storage, permissions, networking where needed) - Automate deployment of data transformations (e.g. dbt CI/CD, staging / production pipelines) - Ensure data platform availability, reliability, security and performance (e.g. enforce roles & permissions in Snowflake, resource monitoring, concurrency/usage optimisation) - Instrument monitoring, logging and alerting of data workflows (Airflow / Kubernetes / dbt jobs) - Collaborate with Data Engineers / Analysts / Architects to define platform capabilities, set standards & best practices around schema design, governance, version control, and performance - Run capacity planning, ensure cost-efficiency, scaling strategy (e.g. concurrency limits in Snowflake warehouse sizing, cluster autoscaling, etc) - Facilitate onboarding of teams to the data platform: document usage patterns, create templates or utilities (for example dbt macros, shared libraries) - Participate in architecture reviews, evaluate new platform tooling (e.g. enhancements to orchestration, transformation frameworks, security strategy, etc) - Troubleshoot critical incidents and participate in incident / post-mortem cycles for platform issues Qualifications - Minimum of 3 years experience building data pipelines with Python (Required) - Minimum of 3 years experience working with data warehouse or other cloud based database technology, with strong proficiency in SQL. (Required) - Experience with Docker / Kubernetes (Required) - Exposure to Infrastructure-as-Code (IaC) such as Terraform or DevOps (Required) - Experience working with dbt (Preferred) - Strong experience with cloud infrastructure: AWS (EC2, ECR, S3, Glue, RDS, etc) or equivalent public cloud provider - Hands-on experience in CI/CD, version control, unit / integration testing for data pipelines - Comfortable working in agile teams, and mentoring others - Strong Communication Skills - Excellent Interpersonal Skills - Excellent Organizational Skills - Proven Analytical Skills - Ability to communicate complex technical terms in an easy to understand, non-technical manner - Ability to interact effectively with co-workers in a result driven culture - Self-starter, energetic multi-tasker, highly motivated and team player - Ability to engage with and establish trust and rapport with all levels of customers and employees - Agile practitioner experienced in Scrum or Kanban - General knowledge of Apple products and eco-systems - Bachelor's Degree in Mathematics, Computer Science or related field (Required) - A combination of relevant experience and education may be considered Requirements - Participation in ongoing security training is mandatory - Established security protocols will be adhered to, sensitive data will be handled responsibly, and data protection practices are followed, including understanding relevant privacy regulations and reporting breaches - Acknowledging the Jamf Code of Conduct, where applicable security and privacy policies can be found, is a requirement of all roles at Jamf Benefits - Named a 2025 Best Companies to Work For by U.S. News - Named a 2024 Best Technology Company to Work For by U.S. News - Named one of Forbes Most Trusted Companies in 2024 - Named a 2024 Best Companies to Work For by U.S. News - Opportunity to make a real and meaningful impact for more than 75,000 global customers - Support for new innovations and OS releases the moment they are made available by Apple - Work with a small and empowered team where the culture is based on trust, ownership, and respect - Clear career path that enables you to grow under supportive leadership and management - Access to the Jamf Engineering blog for insights on innovative projects - Pay Transparency Range: $85,100 — $181,700 USD

• Define the target architecture for Customer360 on Snowflake or Databricks, including ingestion patterns, modeling standards, and governance. • Design and lead the Golden Record / identity resolution approach (deterministic matching first), including identifiers, survivorship rules, confidence scoring. • Create the canonical customer model (core entities/relationships) and align marts/domains (e.g., insurance, cards, loans) into a unified customer layer. • Establish data quality frameworks: checks (null/uniqueness/RI/thresholds), monitoring/alerts, lineage/source-of-truth mapping, and data SLAs. • Define activation-ready outputs (customer attributes, segments, eligibility indicators) and support low-latency enablement patterns where needed.