Data Forward Deployed Engineer - Applied Data & ML Engineering

Role Summary

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.

You will 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, and you will feed proven patterns back into the Tessera platform.

The role requires strong experience in SQL, Python, data modeling and applied machine learning, combined with the judgment to own ambiguous client problems end to end. This is a mid- to senior-level role.

This role owns delivery quality on your engagements: validation, reconciliation to source and stakeholder sign-off.

Key Responsibilities

• DEPLOY WITH CLIENTS
• Embed with client teams to scope data and ML problems, map source systems and deliver working outcomes in weeks.
• Own delivery quality, including validation, reconciliation to source and stakeholder sign-off.
• Turn repeated client patterns into reusable components for the Tessera platform.
• BUILD AND HARMONIZE ENTERPRISE DATA
• Design and develop scalable ETL/ELT pipelines using SQL, Python and dbt to ingest and transform data from enterprise platforms, including ERP, CRM, HCM and finance systems.
• Design canonical enterprise data models and cross-system mapping rules that standardize master and transactional data.
• Implement validation, reconciliation and monitoring to resolve duplicate, inconsistent and legacy data.
• APPLY MACHINE LEARNING
• Engineer features and build models on enterprise data for use cases such as forecasting and anomaly detection.
• Deploy models to production and monitor them.

Required 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.

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.

Preferred Qualifications

• 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.

Additional information

If you want to turn fragmented enterprise data into trusted data products and production ML models, and to build it with us, we would like to hear from you.

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