• Build scalable, reliable data systems while maintaining SLOs for business-critical pipelines. • Partner with other internal teams such as Product, Sales Ops, and Finance to deliver high-impact data solutions. • Support self-service tooling and a data-driven culture across Pantheon's teams. • Contribute to the technical strategy and operations of Pantheon's data platform. • Stay up-to-date with industry trends and technologies in data engineering, analytics, and modern data platforms. • Continuously improve our standard of engineering excellence by implementing best practices for data architecture, testing, pipeline reliability, and documentation.

• Own and maintain all CI/CD release pipeline YAML files across GitHub and Azure DevOps (ADO), ensuring pipelines are well-structured, version-controlled, and consistently documented. • Design, build, and enhance automated deployment pipelines for non-production and production hosted environments, supporting multiple customer environments. • Manage pipeline branching strategies, approval gates, environment-specific variable groups, and deployment conditions in alignment with Change Management (CAB) processes. • Continuously improve pipeline reliability, execution speed, and error handling to reduce deployment risk and increase release frequency. • Maintain pipeline-as-code standards, ensuring all pipeline definitions are peer-reviewed and stored in source control alongside application code. • Coordinate and execute deployments across all hosted environments development, test, staging, and production - with a focus on consistency, repeatability, and minimal customer impact. • Manage environment-specific configuration files and secrets, ensuring proper separation of configuration from code and secure handling of sensitive values. • Implement blue/green, rolling, or canary deployment strategies where applicable to reduce downtime and deployment risk. • Partner with DBA, IT, and application teams to align deployment windows with maintenance schedules and change management requirements. • Maintain deployment runbooks, rollback procedures, and post-deployment validation checklists for all hosted environments. • Manage Azure DevOps organizations, projects, pipelines, service connections, agent pools, and variable groups. • Define and enforce Git workflow standards (branching strategies, pull request policies, merge requirements) across the hosting operations team. • Integrate GitHub and ADO with downstream tools and services, including Azure cloud services, monitoring platforms, and notification channels. • Maintain pipeline and repository security, ensuring secrets and credentials are managed via Azure Key Vault or equivalent secret management tooling. • Develop and maintain Ansible playbooks and roles for automated configuration management across hosted Linux and Windows environments. • Use Ansible to enforce baseline configurations, manage software installations, apply patches, and maintain environment consistency across all hosted nodes. • Integrate Ansible automation into CI/CD pipelines to enable fully automated end-to-end deployment and configuration workflows. • Maintain an organized Ansible inventory structure and variable hierarchy supporting multiple environments and customer configurations. • Design and implement proactive monitoring solutions for hosted environments using internal tools, 3rd party developed tools, and alerting frameworks. • Build and maintain dashboards that provide real-time visibility into environment health, pipeline execution status, deployment success rates, and infrastructure performance. • Define alerting thresholds and escalation rules to detect and surface issues before they impact customers, integrating alerts with Microsoft Teams, PagerDuty, or equivalent notification channels. • Implement log aggregation and centralized log management to support troubleshooting, incident response, and compliance reporting. • Provision and manage Azure resources (VMs, VNets, NSGs, Storage Accounts, Key Vaults, App Services) supporting the hosted platform, using Infrastructure as Code (IaC) tools such as Bicep or Terraform. • Support cloud cost management by identifying over-provisioned resources, rightsizing recommendations, and pipeline-driven resource lifecycle management. • Participate in Azure Landing Zone design and implementation, ensuring hosted environments comply with organizational standards for security, networking, and governance. • Collaborate with infrastructure and security teams on Azure policy enforcement, RBAC configuration, and compliance posture. • Work closely with the Engineering, DBA, IT, and customer success teams to align release schedules, coordinate change management, and resolve deployment blockers. • Maintain internal documentation in Confluence for pipeline architecture, deployment procedures, configuration standards, and monitoring runbooks. • Participate in on-call rotation to support production deployment incidents and environment availability issues.

• Own the lifecycle of internal AI APIs, SDKs, and developer tooling to abstract LLM complexity • Build and maintain CI/CD-integrated AI systems including code review automation and test generation pipelines • Drive product development acceleration by integrating AI capabilities directly into existing collaborative workflows • Design evaluation frameworks and automated testing pipelines to ensure quality of AI system outputs • Build observability tooling to monitor model behavior, token costs, latency, and production output quality • Establish shared prompt libraries and reusable context management systems • Define and enforce engineering standards for AI system development • Provide technical guidance and conduct architectural reviews of AI features

Role Description We are seeking a Data Engineer to support Machine Learning and AI initiatives. Working closely with the Solution Architect, Data Architect, DevOps, and Application Engineering teams, this role is responsible for ensuring that data within our cloud-based platform is high quality, well-governed, feature-ready, and production-grade to support model training, deployment, and ongoing operations. The ideal candidate has 5+ years of cloud data engineering experience with strong proficiency in Snowflake, Python, and SQL, and solid familiarity with AWS-native data services. Immediate focus is Snowflake-based data engineering, pipeline development, and data quality. Feature engineering, model training support, and MLOps contributions are growth areas that will ramp over time as you become embedded with the team. Key Responsibilities - Data Pipeline Engineering - Design, build, and maintain scalable data pipelines supporting ML/AI workloads. - Engineer pipeline patterns including full loads, incremental loads, change-based loads, and slowly changing dimensions. - Ensure pipelines are reliable, performant, secure, and maintainable; troubleshoot and monitor pipelines within an AWS ecosystem. - Snowflake & Cloud Data Engineering - Perform data transformations in Snowflake using SQL and native Snowflake features. - Design and optimize schemas, tables, views, and materialized views for ML/AI consumption. - Support AWS-native data lake patterns using S3, Glue, Athena, Apache Iceberg, and S3 Tables. - Feature Engineering & Data Preparation - Perform data cleansing, normalization, and enrichment to support ML model development. - Design and implement feature engineering pipelines including aggregation and transformation. - Ensure consistency, reuse, and versioning of features across models and use cases. - Support feature store patterns to enable feature discoverability and reuse. - Collaborate with ML engineers and data scientists to operationalize features into training pipelines. - Model Training & MLOps Support - Support model training workflows, including dataset preparation and scheduled refreshes. - Ensure training datasets and features are reproducible, traceable, and auditable. - Integrate data pipelines into CI/CD workflows; support version control, testing, and deployment of data assets. - Monitor pipeline health, data freshness, and downstream impact on ML/AI systems. Qualifications - 5+ years of hands-on data engineering experience in a cloud environment. Required Skills & Experience - Core Technologies - Python — strong proficiency for data processing and pipeline development. - SQL — advanced skills with hands-on Snowflake transformation experience. - Snowflake — ELT pipeline design, schema optimization, performance tuning, cost management. - PostgreSQL — experience with querying, data modeling, and analytics; familiarity with SQL Server to PostgreSQL migration a plus. - AWS — S3, Glue, Athena, Snowflake integration, and managed relational databases (e.g., Aurora, RDS). - Apache Iceberg / S3 Tables — familiarity with open table format ecosystems. - Streaming ingestion tools (e.g., Kinesis, Kafka, or equivalent). - Workflow orchestration tools (e.g., Airflow, Step Functions, or equivalent). - Pipeline & Data Engineering - Experience with full loads, incremental loads, append-only pipelines, change-based processing, and SCDs. - Data validation, reconciliation, error handling, and restart/recovery patterns. - Data modeling for analytics, ML/AI, and downstream application use cases. - Ability to evaluate pipeline design trade-offs across performance, cost, reliability, and maintainability. - DevOps & Engineering Practices - Structured SDLC experience with CI/CD pipelines for data and ML workflows. - API-based and event-driven data integration patterns. - Distributed data processing environments. - ML/AI Data Foundations - Understanding of data requirements for ML/AI workloads. - Experience preparing training datasets and features from enterprise data lakes. - Familiarity with reproducibility, dataset versioning, and data lineage concepts. - Familiarity with GenAI concepts relevant to data engineering, such as embedding pipelines, vector databases, retrieval-augmented generation (RAG) data flows, or prompt-driven data processing — including awareness of data security and privacy considerations when working with LLMs. Education - Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related technical field. Equivalent professional experience will be considered. Location - Remote Status - Full time position with full company benefits.