[Hiring] AI Data Engineer @Netwrix Corporation

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