AI Data Engineer

About Us At Netwrix, our mission is to revolutionize data security by placing identity at the core - providing unparalleled visibility and control. Engineered and supported by over 900 highly talented, motivated employees and hundreds of trusted partners in nearly every geography, Netwrix solutions are relied upon daily by security professionals across more than 13,500 organizations in over 100 countries around the world. Over the past two decades, Netwrix has expanded its market presence through innovation, organic growth, and strategic acquisitions, and are proud to be backed by renowned private equity firms, TA Associates and Centerbridge Partners. Netwrix maintains a global presence, fostering a remote-first work environment while encouraging and facilitating frequent face-to-face interaction with colleagues, customers, and partners. Position Overview 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. Key Responsibilities 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, r ... (truncated, view full listing at source)