Overnight Infor Ln Developer Jobs in New York (NOW HIRING)

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, MuleSoft, 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 Sage ERP, Baan / Infor LN, Oracle ERP, SaaS ERP platforms (such as NetSuite), 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

Key Responsibilities
Build ERP Data Integration Pipelines
Design and develop scalable ETL/ELT pipelines to ingest and transform data from enterprise systems including:

• Sage ERP (e.g., Sage X3, Sage Intacct, Sage 300)
• Baan / Infor LN
• Oracle ERP / Oracle Fusion
• SaaS ERP platforms such as NetSuite
• Legacy ERP and adjacent enterprise systems

Implement transformation pipelines using SQL and Python.
Implement Data Harmonization Logic
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

Design Enterprise Data Models
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

Translate Functional Requirements into Data Logic
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

Improve Data Quality and Governance
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

Required Skills & Experience
Professional Experience

• 5-8+ years of experience in Data Engineering, Data Integration, or Data Platform development
• Experience working with enterprise ERP systems, such as:
  
  • Sage ERP platforms
  • Baan / Infor LN
  • Oracle ERP / Oracle Fusion
  • NetSuite or other SaaS ERPs
• Experience supporting ERP transformation, migration, or multi-system integration initiatives

Technical Skills
Programming

• Advanced SQL
• Strong Python for data engineering workflows and pipeline development

Data Engineering

• Experience building ETL/ELT pipelines
• Experience processing large-scale enterprise datasets
• Familiarity with modern data architectures and distributed processing

Data Modeling

• Strong understanding of:
  
  • Relational data models
  • Dimensional models
  • Canonical enterprise data models

ERP Data Knowledge
Understanding of ERP business domains including:
Finance

• General Ledger
• Accounts Payable / Receivable
• Financial transactions

Supply Chain

• Materials and product data
• Procurement and vendor management
• Inventory and logistics

Enterprise Master Data

• Customers
• Vendors
• Products
• Organizational structures

Behavioral & Problem-Solving Expectations
Systems Thinking
Ability to understand complex enterprise systems and business processes, and design data solutions that work across multiple platforms.
Translating Business Logic into Technical Solutions
Capability to work with functional teams and convert business rules into scalable data transformation logic.
Data Problem Solving
Strong analytical skills to diagnose and resolve enterprise data challenges.
Collaboration
Comfortable working with cross-functional teams including engineers, ERP functional consultants, and business stakeholders.
Ownership and Execution
Ability to take ownership of complex data problems and deliver production-grade solutions that scale across enterprise environments.
No third party may recruit, solicit candidates, publish job opportunities, use Tessera Labs' name or branding, or represent that they are acting on behalf of Tessera Labs without prior written authorization. Any such activity conducted without explicit written consent is strictly prohibited.