Role Description The AI Data Engineer designs, builds, and operates enterprise‑grade data and AI platforms using GitOps principles. This role combines data engineering, AI enablement, platform engineering, and IT Operations, with a strong emphasis on stability and repeatability. This role directly supports and enables Netwrix products and internal platforms, ensuring that AI and data capabilities align with Netwrix’s security‑first, governance‑driven mission. The AI Data Engineer will work with data generated by or integrated into Netwrix solutions such as: - Netwrix Data Security Platform components, including data access governance, data classification, auditing, and identity‑centric security telemetry. - Platform Governance products (Drata, Salesforce, and NetSuite), which generate configuration, change, and audit data requiring structured ingestion and analysis. - Identity, endpoint, and infrastructure security products (e.g., Active Directory security, endpoint protection, privileged access, configuration management). - Internal AI Agents and Experience Platforms where data must be securely scoped, versioned, and observable across multiple domains/tenants. Qualifications - Bachelor’s Degree in Computer Science, Data Engineering, Engineering, or equivalent practical experience. - 5 - 7 years of experience in data engineering, platform engineering, or infrastructure roles. - Strong proficiency in Python and SQL, with working fluency in JSON, YAML, and shell scripting. - Experience using Git-based workflows, Infrastructure as Code, and CI/CD pipelines to build and operate data and AI platforms in production environments. - Experience operating workloads in Azure and AWS. - Has performed direct and operational applications of large language models (LLMs) and GenAI platforms (OpenAI, Anthropic Claude, Google Gemini) within enterprise controlled environments. Requirements - GitOps‑Driven Platform & Pipeline Engineering (GitHub, Azure DevOps, Terraform) - Design, build and operate data and AI platforms as code, using Git‑based workflows as the source of truth. - Implement pull‑request‑driven change control, automated testing, and CI/CD pipelines. - Define and maintain Infrastructure‑as‑Code for data and AI systems to ensure consistency, traceability, and rollback capability. - AI & ML Data Pipeline Engineering (Azure ML Feature Store, Databricks Feature Store) - Design and maintain scalable ETL/ELT pipelines that support: - AI/ML model training and retraining - Feature engineering and feature stores - Batch and near‑real‑time inference workflows - Design pipelines backwards from business requirements while accounting for data freshness, latency, and reliability. - GenAI & RAG Enablement (Azure OpenAI and AI Search, internal Netwrix data sources; internal AI agents, secured APIs) - Support Retrieval‑Augmented Generation (RAG) and internal AI agents by curating, indexing, and refreshing select data sources. - Build and operate pipelines for: - Embedding generation and lifecycle management - Vector database ingestion and maintenance - Context retrieval and prompt‑adjacent data flows - Data Quality, Governance & Observability (Azure Monitor, ML monitoring, Application Insights) - Implement proactive monitoring for: - Data quality and schema integrity - Pipeline performance and failure modes - Distribution shifts and data drift impacting AI systems - Integrate security, privacy, and compliance controls directly into pipelines by design. Must partner with both Product and Corporate Security teams. - Maintain clear, auditable data lineage, ownership, and documentation. - MLOps & Production Readiness (Azure ML Model Registry, Runbooks, operational handoff documentation) - Partner with Product and Engineering teams to operationalize models by: - Integrating data pipelines into MLOps workflows - Supporting model versioning, retraining, and rollback strategies - Enabling observability across data and model performance - Ensure AI systems/integrations are supportable by IT Operations and Solutions team members; train or provide guidance at a regular cadence. - Cloud & Platform Engineering (Azure Storage, Azure Kubernetes Service, Azure Container Registry) - Build and operate Azure‑based data and AI platforms, including storage, compute, orchestration, and containerized services. - Optimize platforms for cost efficiency, performance, reliability, and scale in a global, mostly remote work environment. - Support hybrid or restricted environments where AI systems must meet enterprise or regulatory constraints. - Cross‑Functional Collaboration - Work closely with: - IT Operations & Platform Engineering (Intune, Entra ID) - Security & Governance teams (Netwrix, Drata) - Data Science and AI Engineering (Azure ML, Azure OpenAI) - Product and business stakeholders (Salesforce, NetSuite) - Translate AI and business requirements into durable, enterprise‑ready architectures. - Produce clear architecture diagrams, runbooks, and operational documentation. Benefits - Competitive Health Benefits - Continuous Learning and Development Opportunities - Team-Oriented, Collaborative, and Innovative Work Environment - Regular Company Town Halls to Keep You Informed - Opportunities for Career Growth and Advancement Company Description Netwrix Corporation and its wholly owned subsidiaries are Equal Opportunity Employers (EEO) and welcome all applicants for employment without regard to race, color, religion, sex, national origin, age, disability, veteran status, or any other protected characteristic under applicable law. Please let us know if you require any accommodation.

• Frame ambiguous compliance and risk problems as well-defined data and modeling tasks • Build, evaluate, and iterate on machine learning models and LLM-powered features • Design experiments and define metrics that measure real impact on customer workflows • Apply rigorous evaluation, including accuracy, explainability, and bias considerations appropriate to a regulated domain • Build and maintain data and ML pipelines for training, inference, and monitoring • Deploy models and AI features into production and monitor their performance over time • Collaborate with engineering to integrate models into the Themis platform reliably and at scale • Explore and prepare data, build features, and ensure data quality and integrity • Translate data and model findings into clear recommendations for product and leadership • Partner with Product to identify high-value opportunities for ML and AI

• Collaborate with teams of IT/BI and non-IT personnel to create, manage, maintain and develop enterprise data framework and pipelines. • Provide hands-on leadership and feedback on projects and production systems. • Provide training, guidance, and thought leadership to the Data Engineering team. • Take a leadership role in large-scale corporate projects such as integrations and major customer impacting system upgrades. • Expand and optimize data pipeline framework across multiple applications and systems. • Collaborate with the BI team and business analysts to review requirements and translate to ETL design and implementation. • Understand ETL technical approaches and designs. • Provide support for existing data pipeline frameworks. • Diagnose and troubleshoot problems with existing data structures, ETL processes and logic. • Take a leadership role in setting organizational data management and architecture strategy. • Manage projects and workload according to FMB standard technology.

Role Description Esta posição é responsável por projetar, desenvolver e manter plataformas de dados corporativos que possibilitam insights sobre dados de clientes. O(a) Engenheiro(a) de Dados utilizará conhecimentos em SQL e Python para executar projetos e tarefas de alta qualidade, com elevado nível de precisão e dentro dos prazos estabelecidos. - Projetar, codificar, testar, corrigir e documentar programas e scripts utilizando padrões e ferramentas acordados, garantindo resultados bem estruturados. - Assegurar a qualidade dos dados e implementar ferramentas e frameworks para automatizar a identificação de problemas de qualidade de dados. - Realizar perfilhamento de fontes de dados e desenvolver processos ETL, com conhecimento dos fundamentos de modelagem de dados, utilizando SQL, Python e ferramentas de suporte a ETL/ELT. - Auxiliar a gestão na criação de estimativas e propostas para clientes. - Planejar soluções eficazes para armazenamento, segurança, compartilhamento e publicação de dados dentro da organização. - Criar e atualizar documentações de fluxos de processos e regras de negócio. - Manter fluxos existentes atualizados e processar grandes volumes de dados conforme necessário. Qualifications - Graduação em Ciência da Computação, Estatística, Matemática ou área relacionada. - 3 a 5 anos de experiência em Engenharia de Dados e/ou Data Warehousing. - Experiência em projetar e desenvolver pipelines de dados ETL/ELT. - Proficiência na escrita de consultas SQL avançadas. - Proficiência em Python e em diferentes bibliotecas Python. - Forte capacidade analítica, com habilidade para compreender e comunicar o significado dos dados apresentados. - Excelentes habilidades matemáticas, estatísticas e de lógica. - Excelente capacidade de raciocínio. - Perfil entusiasta, altamente motivado, com habilidade para aprender rapidamente. - Experiência na criação de workflows ETL no Databricks. - Compreensão de Apache Spark. Requirements - 3 a 5 anos de experiência em operações automotivas ou em negócios similares (ambiente dinâmico). - Experiência na construção de soluções de dados em nuvem Azure ou AWS e em migração de ambientes on‑premise para a nuvem. - Experiência prévia em atendimento a clientes externos. - Familiaridade com administração de MS SQL Server. - Experiência com repositórios GIT. - Experiência com FTP e APIs. - Forte capacidade de comunicação e conhecimento prático de metodologias ágeis, incluindo conceitos de DevOps. - Experiência com ferramentas de visualização de dados (Tableau, Power BI, etc.). Other Competencies and Characteristics - Retenção de Conhecimento – Para entregar trabalho da mais alta qualidade, os(as) analistas precisam aprender e memorizar uma grande quantidade de informações. - Atenção aos Detalhes – Os(as) analistas devem ser capazes de focar nos detalhes para identificar e isolar problemas. - Habilidades Organizacionais – É essencial que os(as) analistas sejam organizados(as) e capazes de acompanhar múltiplos projetos simultaneamente. - Flexibilidade – Devem ser capazes de lidar com diversas tarefas ao mesmo tempo e se adaptar a mudanças de prioridade. - Agilidade – Devem trabalhar de forma rápida e eficiente, sem comprometer a qualidade das entregas. - Orientação para Soluções – Devem ser capazes de encontrar soluções viáveis para qualquer problema que possam enfrentar. Language Skills - Proficiência profissional completa em inglês é obrigatória. Physical Requirements - Durante a execução das atividades deste cargo, o(a) colaborador(a) é regularmente solicitado(a) a realizar chamadas, videoconferências, enviar e-mails, mensagens e se comunicar com concessionárias e colegas de trabalho. - Acomodações razoáveis poderão ser fornecidas para permitir que pessoas com deficiência possam exercer as funções essenciais. Work Environment - Ambiente remoto e/ou escritório. - O nível de ruído no ambiente de trabalho geralmente é moderado. - Acomodações razoáveis podem ser fornecidas para permitir que pessoas com deficiência possam desempenhar adequadamente essas funções.