• Apoiar a construção, modelagem e manutenção do nosso data lake nas camadas Raw, Bronze e Silver. • Desenvolver e manter pipelines de dados robustos, escaláveis e bem documentados. • Automatizar processos de coleta, transformação, entrega e distribuição de dados internos e externos. • Garantir observabilidade, qualidade, consistência e governança dos dados. • Monitorar e otimizar o desempenho de pipelines, workloads e processos de transformação. • Trabalhar em parceria com analista, cientista de dados, engenheiro e stakeholders de produto. • Apoiar áreas de negócio com dados confiáveis para tomada de decisão. • Aplicar boas práticas de versionamento, testes e CI/CD em projetos de dados.

• Design, build, and scale robust ETL/ELT pipelines optimized for AI workloads, including RAG, fine-tuning, and batch inference. • Transform unstructured data sources such as PDFs, logs, and transcripts into structured and vectorized formats suitable for LLM consumption. • Maintain and automate the data-to-model lifecycle, ensuring AI knowledge bases remain synchronized with changing business data. • Develop and maintain real-time feature pipelines that support low-latency AI and machine learning applications. • Integrate data platforms with Kafka and other event-driven systems to enable real-time processing and AI-driven responses. • Manage and optimize Feature Stores to ensure consistency between model training and production environments. • Implement automated data quality controls and validation processes to ensure the reliability and accuracy of AI training and inference data. • Establish and maintain data lineage frameworks to provide traceability, auditability, and regulatory compliance across data workflows. • Enforce data security, privacy, and governance standards, including PII protection and compliance with industry regulations. • Manage data movement and synchronization across on-premises systems, cloud platforms, and data warehouses. • Optimize data storage and retrieval strategies for Vector Databases to support high-performance RAG and AI search workloads. • Collaborate with Data Scientists, ML Engineers, Software Engineers, and business stakeholders to deliver scalable AI data solutions.

Title: Lead Platform Engineer - Data & AI Locations: Boston, MA; Denver, CO; Detroit, MI Job Description: Your role at Dynatrace The Lead Platform Engineer, Data & AI is responsible for designing and developing solutions for the operational health, governance, and continuous improvement of the data platform, owning Snowflake administration, data pipeline/dbt administration, and the automation of the broader data technology stack from end to end. This is a hands-on operational and engineering role. The right candidate combines deep platform administration skills with a systems-thinking mindset: they identify manual processes and eliminate them through automation, maintain platform reliability at scale, and are beginning to explore how AI agent capabilities can extend the value delivered to data consumers. Key Responsibilities: - Snowflake Platform Administration: Own Snowflake administration end-to-end across development, staging, and production environments consolidating instances, covering warehouse sizing and cost governance, query optimization, object lifecycle management, RBAC design, Row-Level Security policy implementation, and access provisioning automation. Maintain platform health through proactive monitoring, manage Snowflake feature adoption (Streams, Tasks, Dynamic Tables, Snowpipe), and act as the primary escalation point for Snowflake performance and access issues. - dbt Administration & Governance: Administer the dbt project and deployment infrastructure - owning project configuration, model architecture standards, environment and job management, test coverage enforcement, documentation requirements, and model promotion workflows across environments. Monitor dbt run health and lineage, configure observability tooling (Elementary or equivalent), and partner with analytics engineers to review and certify models for production use. - Data Stack Automation & Operations: Automate the operational layer of the data technology stack - including ETL/ELT tool administration (Fivetran, etc), RBAC and access provisioning via Terraform or scripting, alerting and notification pipelines for data quality and platform health, and CI/CD release workflows for dbt and platform configuration. Reduce manual operational toil by building reusable automation frameworks that make routine platform tasks fast, auditable, and self-service where appropriate. This is a remote eligible position. Candidates who sit within a 45 mile radius of Boston, MA; Denver, CO; Detroit, MI will be required to work hybrid (2 days per week in office). All candidates will be required to work EST hours. What will help you succeed Minimum Requirements - 12+ years hands-on data platform engineering - 6+ years of hands-on data platform experience, with direct ownership of a Snowflake environment at production scale. Preferred Requirements - Deep Snowflake DBA skills: virtual warehouse sizing, auto-suspend and scaling policy configuration, multi-cluster warehouse management, and credit cost governance. - Snowflake RBAC design: functional role hierarchy design, privilege grants, service account management, and systematic access provisioning - not one-off manual grants. - Row-Level Security implementation using Snowflake row access policies for multi-tenant or restricted datasets. - Snowflake feature ownership: Streams, Tasks, Dynamic Tables, Snowpipe, External Stages, Data Sharing, and Secure Views in production workloads. - Query profiling and optimization: reading query profiles, identifying bottleneck operators, applying clustering keys, and resolving warehouse contention. - Deep experience administering dbt project end-to-end - model architecture (staging / intermediate / marts), incremental and snapshot patterns, and environment separation - dbt Cloud or dbt Core deployment administration: job scheduling, environment variable management, run monitoring, failure alerting, and model promotion across dev/staging/prod. - Test coverage governance: enforcing schema tests, data tests, and source freshness checks as mandatory gates before production promotion. - dbt observability: configuring and maintaining Elementary, or equivalent tooling - including artifact-based monitoring, model health dashboards, and freshness tracking against Snowflake account usage - Experience partnering with analytics engineers to review model design, optimize underperforming models, and maintain lineage and metadata for downstream governance. - Platform-level administration of Fivetran or Matillion: connector governance, sync scheduling, schema drift policy, user and environment management, and operational escalation handling. - Alerting and notification automation: configuring and owning alerts for pipeline failures, data quality breaches, SLA violations, and Snowflake cost spikes - routed to Slack, PagerDuty, email, or ServiceNow with actionable diagnostic context. - CI/CD for the data platform: GitHub Actions or GitLab CI pipelines for dbt, Terraform, and ETL tool configuration with environment promotion gates, automated testing, and rollback on failure. - Strong Python and SQL: used for automation scripting, operational tooling, and platform health monitoring, not just pipeline development. - Hands-on experience enabling Snowflake Cortex AI features, Cortex Analyst semantic model configuration, Cortex Search index setup, and production use of LLM functions - Experience developing analytics agents on behalf of business stakeholders, connecting Snowflake data to Slack or other collaboration tools via bot APIs, webhooks, or slash commands so users can query data and receive insights in their workflow. - Practical experience configuring or fine-tuning LLMs (Cortex, OpenAI, Anthropic Claude, or Azure OpenAI) including prompt engineering, system instruction design, and parameter tuning to improve accuracy on governed data. - Ability to design and maintain a semantic data layer, verified views, curated schemas, and structured metadata that grounds LLM-generated outputs in accurate, governed Snowflake data. - Snowflake SnowPro Core, SnowPro Advanced, or Cortex AI certification. - Experience with multi-agent frameworks (LangChain, LangGraph, or AutoGen) applied to enterprise data workflows. Why you will love being a Dynatracer - A one-product software company creating real value for the largest enterprises and millions of end customers globally, striving for a world where software works perfectly. - Working with the latest technologies and at the forefront of innovation in tech on scale; but also, in other areas like marketing, design, or research. - A team that thinks outside the box, welcomes unconventional ideas, and pushes boundaries. - An environment that fosters innovation, enables creative collaboration, and allows you to grow. - A globally unique and tailor-made career development program recognizing your potential, promoting your strengths, and supporting you in achieving your career goals. - A truly international mindset that is being shaped by the diverse personalities, expertise, and backgrounds of our global team. - A relocation team that is eager to help you start your journey to a new country, always there to support and by your side. - Attractive compensation packages and stock purchase options with numerous benefits and advantages. Compensation and Rewards DOE, salary $140K - $163K, plus Health, Dental, Life, STD, LTD, 401K, PTO. Total compensation may vary depending on candidate experience/education and location. Equal Employment Opportunity All your information will be kept confidential according to EEO guidelines. We offer competitive compensation, company-sponsored premium benefits, medical, dental, vacation/holidays, company matching 401(k) Plan, etc. Dynatrace is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, sex, color, gender identity, religion, national origin, ancestry, citizenship, physical abilities, age, sexual orientation, creed, disability status, veteran status, pregnancy, genetic status, or any other characteristic protected by law. If your disability makes it difficult for you to use this site,

Role Description We are seeking a Data Modeler to design and implement scalable, enterprise-grade data models for an insurance client building a modern cloud-based data platform. The role focuses on structuring data across Raw, Silver, and Gold layers to support batch and real-time ingestion, enable efficient reporting, and drive report rationalization initiatives. The ideal candidate will have strong expertise in data modeling (conceptual, logical, physical) and experience working with Snowflake, DBT, and modern data pipelines (AWS Glue, Amazon Kinesis). Key Responsibilities - Data Modeling & Architecture - Design conceptual, logical, and physical data models aligned to business needs and insurance domain standards. - Define and implement models across medallion architecture (Raw, Silver, Gold layers). - Create scalable and reusable data structures optimized for analytics and reporting. - Data Transformation & Layer Design - Collaborate with data engineering teams to implement models using DBT. - Design transformation logic to move data across Raw → Silver → Gold layers. - Ensure consistent data definitions, hierarchies, and business rules. - Insurance Domain Data Structuring - Model data for core insurance functions such as: - Policy - Claims - Billing - Customer / Party - Align data models with industry standards and reporting requirements. - Data Integration Alignment - Work closely with engineers handling: - Batch ingestion via AWS Glue - Real-time streaming via Amazon Kinesis - Ensure models support multi-source data ingestion and harmonization. - Reporting & Rationalization Support - Design optimized Gold layer datasets for reporting and self-service analytics (Power BI). - Support report rationalization initiatives by standardizing and consolidating data models. - Enable consistent KPI definitions across reports. - Data Quality & Governance - Define data standards, naming conventions, and metadata structures. - Ensure data consistency, lineage, and traceability across layers. - Support governance, audit, and reconciliation requirements. - Performance & Optimization - Design models with a focus on query performance and scalability. - Work with engineering teams to optimize pipelines and data structures. - Apply best practices for partitioning, clustering, and efficient joins in Snowflake. Qualifications - Strong expertise in: - Data Modeling (Dimensional, 3NF, Data Vault – preferred) - Snowflake (schema design, performance considerations) - DBT (model structuring and transformation logic) - Advanced proficiency in SQL. - Understanding of medallion architecture (Raw, Silver, Gold layers). - Familiarity with: - AWS Glue (batch data pipelines) - Amazon Kinesis (real-time streaming) - Experience designing models for multi-source integrated data platforms. - Understanding of data consumption patterns for reporting and analytics. Requirements - Strong understanding of Insurance domain data models (Policy, Claims, Billing, Underwriting). - Exposure to enterprise data platforms in insurance organizations. - 5–10 years of experience in Data Modeling / Data Architecture. - Experience in cloud-based data platforms and modern data stack. Benefits - Inclusive workplace with equal employment opportunity (EEO) and affirmative action. - Encouragement of individuality, growth, and well-being.