[Hiring] Data Forward Deployed Engineer @Tessera Labs

Role Description

We are looking for a Forward Deployed Engineer (FDE) for our Applied Data & ML capability. You will work directly with enterprise clients to turn fragmented, multi-system enterprise data into trusted data products and production ML models.

• Collaborate closely with client teams, SMEs, and Tessera product engineers to translate complex business logic into data pipelines, canonical data models, and applied ML solutions.
• Feed proven patterns back into the Tessera platform.
• Own delivery quality on your engagements: validation, reconciliation to source, and stakeholder sign-off.

Qualifications

• 5+ years of experience in Data Engineering, ML Engineering, or Applied Data Science.
• Enterprise data harmonization, migration, or analytics experience across ERP, CRM, or finance systems.
• Experience in client-facing, consulting, or forward-deployed roles.
• Advanced SQL, and strong Python for data pipelines and ML workflows.
• Experience building ETL/ELT pipelines and processing large-scale enterprise datasets.
• Relational and dimensional data modeling.
• dbt for transformation, testing, and documentation.
• Cloud platform experience (AWS, GCP, or Azure).
• At least one ML model built, deployed, and monitored in production on business data.
• Feature engineering from structured and time-series enterprise data, and model evaluation and monitoring.
• Working knowledge of core enterprise data domains and business processes, such as finance, supply chain, and master data.
• Systems thinking: the ability to understand complex enterprise systems and business processes, and to turn business rules into data and ML solutions that work across platforms.
• Client ownership: comfortable working directly with client engineers, functional consultants, and business stakeholders, scoping ambiguity, and pushing back with evidence.
• Data problem solving: strong analytical skills to diagnose and resolve enterprise data and model performance challenges.
• Ownership and execution: the ability to take ownership of complex problems and deliver production-grade solutions that scale across enterprise environments.

Requirements

• Hands-on experience with a cloud data platform such as Snowflake or Databricks.
• Distributed processing (Spark / PySpark, DuckDB, or similar).

First-Year Measures of Success

• Month 1: Up to speed on the Tessera platform and delivering on at least one client engagement. Trusted by the client’s data and business stakeholders and by the Tessera team on that engagement.
• Month 2: Your first data product in production, validated and reconciled to source, with stakeholder sign-off. Repeated client patterns captured as reusable components for the Tessera platform.
• Month 3: At least one ML model, for example for forecasting or anomaly detection, deployed and monitored in production on client data. Your reusable components in use on other engagements.

Who This Role Is Not For

If you prefer to work on clean, well-documented datasets inside a single platform team, this is not the role: the data here is fragmented, duplicated, and spread across ERP, CRM, HCM, and finance systems. If you would rather hand a pipeline or model over than own it through validation, reconciliation to source, and client sign-off, the same applies. And if you are not comfortable working directly with client stakeholders, scoping ambiguity, and pushing back with evidence, you will not enjoy this role.