Data Engineer - Enterprise AI, ERP and SaaS

About Tessera Labs

Tessera Labs is redefining how enterprises adopt and operationalize Artificial Intelligence. Backed by Foundation Capital and led by a world-class founding team, we build multi-agent AI systems that automate complex business workflows across platforms such as SAP, Salesforce, Workday, Snowflake, Mule Soft, and more.

Location:
San Jose, CA or New York City

Remote:
Considered; travel required

About Tessera Labs

Tessera Labs is redefining how enterprises adopt and operationalize Artificial Intelligence. Backed by Foundation Capital and led by a world-class founding team, we build multi-agent AI systems that automate complex business workflows across platforms such as SAP, Salesforce, Workday, Snowflake, Mule Soft, and more.

Our mission is to bring real AI automation to the enterprise—delivering speed, precision, and measurable impact. We operate with extreme ownership, move quickly, and build at the frontier of applied AI.

Role Summary

We are seeking a Senior Data Engineer to design and build scalable data solutions that harmonize enterprise data across multiple ERP ecosystems. This role focuses on integrating and standardizing data from SAP (ECC/S4

HANA), Oracle ERP, SaaS ERP platforms (such as Net Suite), and other enterprise systems into unified data models.

The engineer will collaborate closely with ERP functional teams, Functional Data Experts (FDEs), and product engineers to translate complex business logic into robust data transformation pipelines and canonical enterprise data models.

This role requires strong experience in SQL, Python, enterprise data modeling, and ERP data structures, combined with the ability to solve complex cross-system data inconsistencies and harmonization challenges.

Why This Role Matters

Enterprise organizations often operate across multiple ERP systems and business platforms, each with different schemas, definitions, and data semantics. Without proper harmonization, data becomes fragmented and unreliable for operations, analytics, and decision-making.

This role is critical in building the data foundation that enables organizations to operate with consistent, trusted enterprise data across systems.

The Data Engineer in this role will:

• Enable cross-system interoperability between ERP platforms
• Standardize enterprise master and transactional data
• Support ERP modernization and migration initiatives
• Provide the data backbone for enterprise analytics, AI, and automation

• Design and develop scalable ETL/ELT pipelines to ingest and transform data from enterprise systems including:
• SAP ECC / S4
  
  HANA
• Oracle ERP / Oracle Fusion
• SaaS ERP platforms such as Net Suite
• Legacy ERP and adjacent enterprise systems
• Implement transformation pipelines using SQL and Python.

• Design and maintain data harmonization frameworks that standardize enterprise datasets across systems.
• Define and implement cross-system mapping rules for enterprise data domains including:
• Customers / Business Partners
• Vendors / Suppliers
• Materials / Products
• Chart of Accounts
• Cost Centers and organizational structures
• Financial and operational transactions.

• Develop canonical enterprise data models that normalize ERP data across heterogeneous systems.
• Implement logical and physical models supporting:
• relational data platforms
• dimensional analytics models
• enterprise semantic layers.

• Work closely with ERP functional teams and Functional Data Experts (FDEs) to translate business rules into technical implementations.
• Convert functional requirements into:
• SQL transformation logic
• Python-based processing pipelines
• validation and reconciliation frameworks.

• Implement data validation, reconciliation, and monitoring frameworks.
• Identify and resolve enterprise data issues such as:
• duplicate master data
• inconsistent definitions across ERP systems
• incomplete or legacy datasets
• configuration-driven inconsistencies.

• 5–8+ years of experience in Data Engineering, Data Integration, or Data Platform development
• E…