Data Engineer

Join Tessera Labs in New York, NY, a company at the forefront of enterprise AI adoption. As a Data Engineer specializing in Enterprise AI, ERP, and SaaS, you will collaborate with Forward Deployment Engineers to accelerate ERP modernization and AI-driven transformations for clients. Your role will be pivotal in harmonizing data and developing robust data pipelines. This is a full-time, hybrid position with a competitive salary range of $180K - $250K per year, suitable for a mid-level professional.

Key Responsibilities:

• Integrate, reconcile, and standardize structured data across ERP, CRM, finance, and analytics systems.
• Design and implement ETL/ELT pipelines to unify enterprise data for AI use cases.
• Develop logic for data cleaning, transformation, validation, and preparation for operational and analytical workflows.
• Analyze and document complex, often evolving, enterprise schemas and entity relationships.
• Monitor, troubleshoot, and optimize data pipelines for reliable, high-quality data delivery at scale.
• Prepare structured datasets for multi-agent AI platforms, orchestration engines, and decisioning systems, incorporating lightweight MLOps.
• Collaborate with Forward Deployment Engineers, architects, and client teams on complex modernization challenges.
• Translate ambiguous requirements and evolving constraints into actionable technical solutions.

Must-Have Qualifications:

• Advanced SQL skills, including complex joins and queries across multi-schema relational databases.
• Proficiency in Python or a similar language for data processing and pipeline development.
• Strong understanding of relational data modeling, schema mapping, and database design principles.
• Experience with enterprise systems like SAP S/4HANA, Salesforce, finance systems, or cloud data warehouses.
• Proven experience building and maintaining ETL pipelines for structured/tabular data.
• Ability to thrive in dynamic, ambiguous environments.
• Demonstrated success in managing fragmented enterprise data with inconsistent schemas and duplication.

Preferred Qualifications:

• Familiarity with distributed data processing (e.g., PySpark).
• Experience with upstream MLOps for structured datasets.
• Background supporting analytics, ML pipelines, or AI workflows.