Data Engineer - Enterprise AI, ERP and SaaS

📋 Description

• • Tessera Labs is at the forefront of revolutionizing enterprise adoption and operationalization of Artificial Intelligence, backed by leading investors and a visionary founding team. We specialize in building sophisticated multi-agent AI systems designed to automate intricate business workflows across a spectrum of critical enterprise platforms, including SAP, Salesforce, Workday, Snowflake, and MuleSoft. Our core mission is to deliver tangible AI automation to businesses with unparalleled speed, precision, and measurable impact. We foster a culture of rapid progress, extreme ownership, and innovation at the bleeding edge of applied AI.
• • This Data Engineer role is pivotal in empowering our Forward Deployment Engineers (FDEs) to accelerate ERP modernization and AI-driven transformations for our enterprise clientele. You will play a direct role in expediting migration processes, ensuring operational continuity, and enabling data-driven decision-making. By shaping the foundational data infrastructure for enterprise-scale AI and analytics solutions within complex landscapes, you will directly contribute to accelerating business outcomes. This position offers a unique opportunity to work at the intersection of enterprise AI, ERP transformation, and multi-agent automation, where your data engineering expertise will be instrumental in achieving significant business impact.
• • As a Data Engineer, your primary focus will be on data harmonization, seamless cross-system integration, and robust pipeline development. You will ensure that our advanced AI solutions and critical enterprise workflows are consistently powered by clean, reliable, and meticulously structured data. This role emphasizes core data engineering principles, including Extract, Transform, Load (ETL) processes, relational schema modeling and mapping, efficient data joins, comprehensive data cleaning, and the development of sophisticated pipeline logic for structured and tabular data. A lightweight upstream MLOps component is integrated into the role, specifically focused on structured datasets, which may involve leveraging distributed processing frameworks like PySpark or employing advanced ML data engineering techniques. It is important to note that this role does not involve downstream responsibilities such as model training, model serving, or deployment.
• • This position demands a deeper understanding of ERP-centric data structures than typically found in a standard ML data engineering role. However, it also requires strong, versatile engineering skills to construct scalable, production-grade data pipelines. Candidates who possess SAP data expertise coupled with modern data engineering or ML-enablement experience are highly sought after. We are also open to candidates who demonstrate significant strength in one area and possess a strong aptitude and eagerness to learn the other.
• • Key responsibilities include:
• • **Data Harmonization:** Integrating, reconciling, and standardizing structured data from diverse enterprise systems such as ERP, CRM, finance, and analytics platforms to create a unified and consistent data view.
• • **Cross-System Pipeline Architecture:** Designing, building, and implementing robust ETL/ELT pipelines that effectively unify data across disparate enterprise systems, specifically tailored to support AI-driven use cases and complex business transformations.
• • **Data Transformation & Validation:** Developing sophisticated logic to clean, transform, validate, and meticulously prepare structured and tabular datasets, ensuring their readiness for critical operational and analytical workflows.
• • **Schema Interpretation:** Analyzing and deciphering complex enterprise schemas, including those that are poorly documented or subject to frequent evolution, and meticulously documenting entity relationships and dependencies across different systems.
• • **Pipeline Reliability:** Proactively monitoring, troubleshooting, and optimizing data pipelines to guarantee consistent, high-quality data delivery at enterprise scale, minimizing downtime and ensuring data integrity.
• • **AI Enablement:** Preparing structured datasets in formats optimized for multi-agent AI platforms, orchestration engines, and advanced decisioning systems, incorporating lightweight upstream MLOps practices where applicable to enhance data readiness for AI consumption.
• • **Cross-Functional Collaboration:** Engaging directly with Forward Deployment Engineers (FDEs), solution architects, and client-side technical teams to collaboratively address and resolve complex enterprise modernization challenges.
• • **Problem Solving Under Ambiguity:** Deconstructing ambiguous requirements and rapidly evolving constraints into clear, actionable, and technically sound solutions, demonstrating agility and strategic thinking in dynamic environments.