• Collaborate with business units to understand data needs and translate them into effective reporting solutions by gathering requirements and delivering high-quality data solutions that support operational and strategic goals • Develop, maintain, and optimize SSRS reports and Power BI dashboards to provide actionable insights • Write and troubleshoot SQL queries to extract, transform, and analyze data from various sources • Ensure data accuracy, consistency, and integrity across all reporting platforms

• Support new and existing customers and their onboarding of IP data • Perform data mapping and manage complex datasets with high level of attention to detail • Migrate customers’ data into our preferred format • Coordinate and receive inputs needed from both customers and internal stakeholders • Maintain accurate customer records. • Resolve issues directly or bring in other internal resources to ensure all customer issues are resolved to the customer’s satisfaction in a timely and careful manner. • Stay current with system changes and updates. • Work closely with customer success, product, and other internal team leads to improve customer experience

• Define Data Platform Strategy: In partnership with senior leadership, define the vision, roadmap, and operating model for AssetWatch’s data platform, ensuring it supports analytics, product development, and AI initiatives across the organization. • Lead the Data Platforms Organization: Build, lead, and develop the data platforms team, establishing clear goals, accountability, and performance expectations. Develop managers and engineers while fostering a culture of ownership, collaboration, and continuous improvement. • Set Organizational OKRs: Partner with executive leadership to define strategic OKRs for the data platform organization and translate them into measurable goals for teams and individuals. • Own Data as a Company Asset: Establish the governance framework that ensures data across the organization is well-defined, trusted, and properly managed, including dataset ownership, data definitions, lineage, access controls, and lifecycle management. • Data Platform Architecture: Design and evolve AssetWatch’s cloud data platform using AWS technologies such as S3, Glue, Athena, Redshift, and Lambda to support scalable analytics, reporting, and AI workloads through MCPs. • Data Ingestion & Pipelines: Oversee the development and reliability of data pipelines ingesting information from product telemetry, APIs, and enterprise SaaS systems. • Curated Data & Data Modeling: Lead the creation of trusted datasets used for reporting, operational decision making, and machine learning. • AI-Ready Data Infrastructure: Ensure the company’s data platform is structured to support AI and machine learning initiatives by enabling reliable, well-modeled data inputs for ML pipelines and advanced analytics. • Enterprise Systems Data Integration: Partner closely with Enterprise Applications leadership to ensure systems such as Salesforce, finance platforms, and customer success tools integrate cleanly into the data platform. • Data Quality & Reliability: Establish monitoring and operational processes that ensure data accuracy, pipeline reliability, and data freshness. • Security, Privacy & Compliance: Work with IT and Security teams to ensure the data platform meets SOC2 and privacy requirements including access control, encryption, audit logging, and retention policies. • Operational Efficiency: Eliminate manual reporting workflows and redundant integrations by creating scalable, automated data pipelines and shared data models. • Cross-Functional Leadership: Lead highly visible initiatives that align teams across engineering, product, operations, and leadership to improve data reliability and organizational decision-making.

Role Description We are looking for a Principal GenAI Data Engineer to join our IT Data Strategy team. This role is fully remote within the US, reporting to the Senior Manager, Enterprise AI Data Platform. We are seeking an experienced technical leader to drive the design and implementation of enterprise-grade Generative AI data ingestion, knowledge preparation, and platform architectures that enable scalable, production-ready GenAI applications. This role focuses on architecting robust pipelines and platforms for ingesting, processing, governing, and serving structured and unstructured enterprise data for AI/LLM workloads. The ideal candidate combines deep expertise in enterprise data architecture, unstructured data pipelines, GenAI platform engineering, and strong software engineering skills in Python. What you’ll do (Role Expectations) - Architect enterprise-scale GenAI data platforms for ingestion, transformation, enrichment, and serving of structured and unstructured data - Design scalable pipelines for enterprise knowledge ingestion from diverse data sources including documents, SaaS platforms, knowledge bases, collaboration tools, and databases - Define architecture for metadata extraction, chunking, enrichment, embeddings generation, and knowledge preparation workflows - Design AI-ready data models and storage strategies for vector, graph, and hybrid knowledge systems - Architect scalable unstructured data processing pipelines for text, images, PDFs, tables, and multimodal content Who You Are (Success Profile) - You act like an owner. Your passion for the mission fuels your bias for action. - You operate with integrity because you genuinely care about the outcome. - You adapt to what’s needed, navigating seamlessly between high-level strategy and hands-on execution. - You are a problem-solver. You seek out challenges because you are energized by finding solutions. - You lead with integrity. You do the right thing, even when it’s hard. - You think at scale. You connect your day-to-day work to the larger company mission and think globally. - You are a high-trust collaborator. You are ambitious for the team, not just yourself. What We’re Looking for (Minimum Qualifications) - Expert-level Python programming and software engineering capabilities - Experience building distributed/scalable data pipelines for AI workloads - Strong understanding of unstructured data extraction and processing pipelines - Experience with vector databases, graph databases, and metadata/knowledge storage systems - Hands-on experience with clustering, entity recognition algorithms, and modern retrieval strategies (including RAG, search, and agentic AI workflows) What Will Make You Stand Out (Preferred Qualifications) - Deep understanding of AI-ready data platform design principles and the ability to bridge platform/data engineering with GenAI/LLM application requirements - Experience with LLMOps / GenAIOps frameworks such as LangSmith, Evaluation Framework like Arize Phoenix, Weights & Biases, or MLflow - Familiarity with Agent Frameworks like LangGraph, CrewAI, or Google ADK Benefits - Various health plans - Time off plans for vacation and sick time - Parental leave options - Retirement options - Education reimbursement - In-office perks, and more!