Remote job
Data Forward Deployed Engineer – Applied Data & ML Engineering
Job details
About this role
Role overview
Work directly with enterprise client teams to turn fragmented data across business systems into reliable data products and machine learning models used in production. You will own delivery from problem scoping through validation and stakeholder approval, while turning useful patterns from client work into reusable platform components. The role calls for someone comfortable navigating ambiguous data and business problems and collaborating with both technical and business stakeholders.
Responsibilities
- Scope data and machine learning needs with clients, map source systems, and deliver working outcomes. - Build scalable ETL and ELT pipelines with SQL, Python, and dbt to ingest and transform enterprise data. - Create shared data models and mapping rules for master and transactional data across systems. - Add validation, reconciliation, and monitoring to find and address duplicate, inconsistent, or legacy data. - Engineer features, build models for use cases such as forecasting or anomaly detection, and deploy and monitor them in production. - Own delivery quality through source reconciliation and stakeholder sign-off, and feed recurring patterns back into reusable components.
Requirements
- Five or more years in data engineering, machine learning engineering, or applied data science. - Experience with enterprise data harmonization, migration, or analytics across ERP, CRM, or finance systems. - Client-facing, consulting, or forward-deployed experience. - Advanced SQL and strong Python skills for pipelines and machine learning workflows. - Experience with large-scale data pipelines, relational and dimensional modeling, and dbt transformations and testing. - Cloud platform experience and evidence of having deployed and monitored a machine learning model on business data.
Nice to have
- Experience with a cloud data platform such as Snowflake or Databricks. - Distributed data processing with tools such as Spark, PySpark, or DuckDB. - Familiarity with enterprise domains such as finance, supply chain, and master data.