The Data Engineer is responsible for monitoring, expanding, and optimizing our data and data pipeline architecture, as well as optimizing data flow and collection for cross functional teams. The ideal candidate is an experienced data pipeline builder and data wrangler who enjoys optimizing data systems as well as building them from the ground up. The Data Engineer supports our solution architects, data analysts, and data scientists on data initiatives and will ensure optimal data delivery architecture is consistent throughout ongoing projects. They must be self-directed and comfortable supporting the data needs of multiple teams, systems and products. Candidates should have education and/or experience in data modeling, requirements analysis, and best practices in analytics. Experience with BI applications is a definite plus. - Develop, test, and maintain data pipelines to support data integration and processing. - Assist in the design and implementation of data models and data warehousing solutions. - Collaborate with data scientists and analysts to understand data requirements and ensure data is accessible and reliable. - Monitor data pipelines and systems, troubleshooting issues as they arise. - Work with senior data engineers to optimize data processes and infrastructure. - Ensure data quality and integrity through regular audits and validations. - Contribute to the documentation of data processes and best practices

• Build the knowledge layer that sits underneath Superads' AI agents — the systems that give them genuine understanding of a brand rather than just access to its documents. • Design how brand knowledge is represented, stored, and continuously updated: the structures that let an agent reason about a brand the way a senior strategist would, not just retrieve what's been written down. • Develop agentic search capabilities — systems that let agents reason about what they don't know, identify knowledge gaps, and navigate information dynamically rather than relying on fixed retrieval pipelines. • Evaluate and iterate on architectures for knowledge retrieval and reasoning, identifying where current approaches break down and driving more interesting solutions. • Translate emerging AI research and patterns into production-ready systems that measurably improve output quality and reliability. • Work closely with the product and AI engineering teams to ensure the knowledge layer directly improves what users experience — staying connected to product impact even when the work takes you deep into infrastructure.

• Database Design & Management: Design, build, and maintain databases that power Hologram's operations, with a focus on simplicity, reliability, and scalability • Pipeline Development: Build and maintain ETL pipelines that move and transform data reliably, with monitoring and alerts to catch breakages fast • Database Reconstruction & Simplification: Audit existing pipelines and data models, identify complexity, and refactor bad decisions into clean, maintainable solutions — incrementally improving what exists rather than starting from scratch. • Data Modeling & Transformation: Own and evolve our dbt models, ensuring data is clean, well-documented, and usable by people outside of engineering • Analytics & Reporting: Write expert SQL to surface insights across the business — from customer usage trends to operational performance • Business Intelligence: Build and maintain dashboards and reports in Sigma that give internal teams clear visibility into the metrics that matter

• Work with laboratory data sources such as LIMS, ELN, Excel, and SharePoint • Design and build data pipelines using Python and SQL • Transform and structure messy R&D data into clean, well-organized datasets • Process and manage data using Azure and Databricks • Collaborate with cross-functional teams, including scientists and analysts • Ensure data quality, consistency, and usability across systems