AI Data Engineer
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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, 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.
Required 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 Gitbased 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.
Our Values
At Netwrix, our values guide every action:
Next-Level Customer Focus -Customers first, always. We listen, protect, and go the extra mile— because their success is our mission.
Excellence - We set high standards and take pride in delivering exceptional results. We celebrate wins, seek constant improvement, and address shortcomings professionally.
Transparent Ownership - We celebrate our successes, own up to our mistakes, communicate openly, and face challenges head-on with a genuine commitment to doing the right thing.
Winning with Clear Thinking - We value clarity, find straightforward solutions to complex problems, and make swift, effective decisions.
Relentless Innovation - We continually seek better ways to serve our customers and stay ahead. We foster creative thinking, and we embrace new approaches.
Industry-Leading Expertise - We take pride in our expertise and continuously seek to learn and share knowledge, striving to be the trusted experts our customers rely on.
eXceptional Together - We believe in the power of collaboration and diverse perspectives. By valuing each other’s strengths, we achieve outcomes that surpass individual contributions.
Join us in a culture where integrity, respect, and hard work are foundational. Be part of a team dedicated to making a lasting impact.
Why You’ll Love Working at Netwrix
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
We pride ourselves on a culture that truly values employee input across various backgrounds and experiences. We look forward to welcoming new talent who can help us further our mission.
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.
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AI Data Engineer
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, 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.
Required 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 Gitbased 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.
Our Values
At Netwrix, our values guide every action:
Next-Level Customer Focus -Customers first, always. We listen, protect, and go the extra mile— because their success is our mission.
Excellence - We set high standards and take pride in delivering exceptional results. We celebrate wins, seek constant improvement, and address shortcomings professionally.
Transparent Ownership - We celebrate our successes, own up to our mistakes, communicate openly, and face challenges head-on with a genuine commitment to doing the right thing.
Winning with Clear Thinking - We value clarity, find straightforward solutions to complex problems, and make swift, effective decisions.
Relentless Innovation - We continually seek better ways to serve our customers and stay ahead. We foster creative thinking, and we embrace new approaches.
Industry-Leading Expertise - We take pride in our expertise and continuously seek to learn and share knowledge, striving to be the trusted experts our customers rely on.
eXceptional Together - We believe in the power of collaboration and diverse perspectives. By valuing each other’s strengths, we achieve outcomes that surpass individual contributions.
Join us in a culture where integrity, respect, and hard work are foundational. Be part of a team dedicated to making a lasting impact.
Why You’ll Love Working at Netwrix
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
We pride ourselves on a culture that truly values employee input across various backgrounds and experiences. We look forward to welcoming new talent who can help us further our mission.
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.
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