Senior ML Engineer, Data Platform (Medallion/Databricks) Hybrid

Riccione Resources, Inc.; Dallas (TX); Hybrid; USD 160,000 - 185,000; Full time

Job summary

Riccione Resources, Inc. is seeking a Senior Machine Learning Engineer to own the ML & Data Platform, building end-to-end pipelines and overseeing data strategy. You will shape Bronze-Silver-Gold flows, create ML-ready datasets, and deploy scalable solutions in a hybrid Dallas environment.

The role emphasizes Medallion Architecture, Databricks-based tooling, feature stores, and governance. Collaboration with cross-functional teams and mentoring are key expectations.

Qualifications

• 5–7 years of ML and data engineering experience.
• Designed ML-ready architectures and scalable workflows.
• Expert in Medallion Architecture for ML-ready datasets.
• Experienced with Azure Databricks for ML and data pipelines.

Responsibilities

• Lead evolution of the ML & Data Platform and drive innovation.
• Build dynamic ML pipelines from ingestion to deployment and monitoring.
• Design Bronze→Silver→Gold data flows for ML workloads and analytics.
• Develop and maintain ML-ready datasets and a Feature Store.
• Turn business needs into scalable ML solutions.
• Engineer robust ETL/ELT and automated workflows.
• Collaborate to ensure ML solutions deliver business impact.
• Mentor the team and champion engineering excellence.
• Safeguard data governance, security, and performance.
• Create dashboards and models to turn ML insights into action.

Skills

ML engineering

Data platform

Team leadership

Python for ML

SQL for ELT

MLOps

Cross-functional communication

Tools

Azure Databricks

Delta Lake

MLflow

Databricks Workflows

TensorFlow

Power BI

Job description

Riccione Resources, Inc. is seeking a Senior Machine Learning Engineer to own the ML & Data Platform, building end-to-end pipelines and overseeing data strategy. You will shape Bronze-Silver-Gold flows, create ML-ready datasets, and deploy scalable solutions in a hybrid Dallas environment.

The role emphasizes Medallion Architecture, Databricks-based tooling, feature stores, and governance. Collaboration with cross-functional teams and mentoring are key expectations.