Role Description This role is responsible for supervising a team of data engineers in the development, deployment, and maintenance of data pipelines and enterprise data solutions. The role combines direct technical contribution with first-level people leadership, coordinating sprint-based delivery and ensuring team output is consistent with established development, documentation, and governance standards. Responsibilities - Supervises a team of data engineers; supports performance management, facilitates career development conversations, and coordinates day-to-day team operations. - Plans and manages sprint-based delivery of data-products; owns backlog prioritization for the team in partnership with stakeholders. - Develops, evaluates, and tests data pipeline solutions that transform and integrate source data into structured, enterprise-ready data assets materialized in an enterprise data warehouse. - Maintains data catalog entries, monitors source freshness, and leads incident response for team-owned pipelines. - Sets and enforces development standards for the team, including testing requirements, documentation conventions, and deployment processes; reviews and approves pull requests and data engineering outputs for consistency with enterprise patterns. - Owns data governance for the team’s domain, including access control, data classification, PII handling, and usage monitoring, ensuring alignment with applicable privacy requirements. - Reviews data requirements from business and technical stakeholders and surfaces findings and recommendations to support informed delivery decisions. Qualifications - Bachelor’s degree (or international equivalent), required. - 5+ years of related experience, required. - 7+ years of related experience, preferred. - Demonstrated ability to supervise and develop technical team members, including providing structured feedback, setting clear expectations, and fostering accountability within a collaborative team environment. - Strong proficiency in ELT pipeline development, including data transformation, testing, documentation, and materialization patterns consistent with modern data engineering practices. - Advanced knowledge of data governance principles, including data classification, access control, and privacy compliance requirements applicable to enterprise data environments. - Working knowledge of cloud data warehousing platforms and associated ingestion, storage, and pipeline orchestration patterns. - Proven ability to manage competing priorities while balancing hands-on technical contribution with team oversight responsibilities. - Strong analytical and problem-solving skills with attention to detail across pipeline design, data quality validation, and incident resolution. - Effective written and verbal communication skills with the ability to translate technical concepts clearly for non-technical stakeholders. - Ability to travel up to 5% of the time. - Must be 18 years of age or older. - Must successfully complete pre-employment screening process, as required. - Must successfully complete any required training or orientation courses, as needed. Benefits - Work from anywhere – Thryv is a Remote First company! - Competitive medical, dental, and vision plans, plus a wellness program with added incentives. - 401(k) savings plan with company match and employee stock purchase plan. - Continuing education benefits with tuition assistance programs. - One week of paid time off at the end of the year, in addition to our standard paid time off policy. Company Description At Thryv, we’re a team fiercely devoted to the success of local businesses. We’ve been around for over 100 years, always with one goal in mind — helping small businesses compete, win, and succeed. We provide the technology, software and local business automation tools small business owners need to better manage their time, communicate with clients, and get paid, so they can take control of their business and be more successful. - We support businesses across the U.S. and our team members are located across the country, and internationally. - We operate as a work from anywhere company and believe this allows us to be more productive. - Culture is vital at Thryv because it shapes our identity and, therefore, our measurements for growth. - We have an identified set of values that hold all of us accountable paving the way for our company success and our legacy. - All of this helps us deliver results for our clients and creates success for our employees.

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.