• Design, build, and maintain backend services, REST APIs, databases, and big data pipelines that power customer-facing insights and analytics. • Implement and maintain near-real-time stream-based data processing pipelines in collaboration with batch-oriented data refresh workflows. • Develop and evolve a query engine capable of answering complex, cross-deal/account questions and delivering actionable insights for sales managers and executives. • Scale data processing and insights generation pipelines to handle growing volumes of activity data (emails, meetings, transcripts, CRM objects) while managing infrastructure costs. • Collaborate with Engineering and Product teams to translate business and customer needs into robust, well-documented technical solutions. • Follow and promote software development best practices, delivering clean, maintainable, and well-monitored code. • Build internal tooling to enable customer support teams to investigate and resolve support requests in a self-service manner. • Ensure high-quality alerting, dashboards, tracing, and runbooks are in place for all production services.

• Lead technical initiatives for large-scale data modernization and migration. • Define data architectures using Databricks Lakehouse and Microsoft Azure. • Assess legacy environments and determine the best strategy for migrating, refactoring, or decommissioning workloads. • Design modern data pipelines, ETL/ELT processes, and orchestration strategies. • Establish architecture, quality, governance standards, and engineering best practices. • Ensure data quality and consistency throughout the migration process. • Collaborate with architects, Data Engineers, Databricks specialists, and client teams to deliver high-quality outcomes. • Act as the project’s technical reference, supporting architectural decisions and mentoring other engineers. • Contribute to automation initiatives and the use of Artificial Intelligence to accelerate migration processes.

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.