Data Forward Deployed Engineer – Applied Data & ML Engineering

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Tessera LabsEnterprise data and ML

Remote in the U.S.Full-TimeMiddle

Salary180,000 - 233,000 USD per year

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Job Details

Experience: 5+ years of experience in Data Engineering, ML Engineering or Applied Data Science.
Required Skills: AWSPythonSQLGCPMachine LearningAzureData modelingdbt

Have 5+ years of experience in Data Engineering, ML Engineering or Applied Data Science.
Have enterprise data harmonization, migration or analytics experience across ERP, CRM or finance systems.
Have experience in client-facing, consulting or forward-deployed roles.
Demonstrate advanced SQL and strong Python skills for data pipelines and ML workflows.
Have experience building ETL/ELT pipelines and processing large-scale enterprise datasets.
Understand relational and dimensional data modeling.
Have experience with dbt for transformation, testing and documentation.
Have experience with a cloud platform: AWS, GCP or Azure.
Have built, deployed and monitored at least one ML model in production on business data.
Have experience with feature engineering from structured and time-series enterprise data, and model evaluation and monitoring.
Have working knowledge of enterprise data domains and business processes such as finance, supply chain and master data.
Be able to understand complex enterprise systems and translate business rules into data and ML solutions across platforms.
Be comfortable working directly with client engineers, functional consultants and business stakeholders, scoping ambiguity and pushing back with evidence.
Preferred: hands-on experience with Snowflake or Databricks, and distributed processing such as Spark, PySpark or DuckDB.

Embed with client teams to scope data and ML problems, map source systems and deliver working outcomes.
Own delivery quality, including validation, reconciliation to source and stakeholder sign-off.
Turn repeated client patterns into reusable components for the Tessera platform.
Design and develop scalable ETL/ELT pipelines using SQL, Python and dbt.
Design canonical enterprise data models and cross-system mapping rules for master and transactional data.
Implement validation, reconciliation and monitoring to resolve duplicate, inconsistent and legacy data.
Engineer features and build models on enterprise data for use cases such as forecasting and anomaly detection.
Deploy ML models to production and monitor them.

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