Databricks Machine Learning Engineering

We are seeking a Senior ML Engineer with strong data engineering foundations and deep experience building ML?ready data pipelines within a Medallion Architecture. This role will lead technical initiatives, shape the ML and data engineering strategy, and help build a modern data platform that powers advanced analytics, predictive modeling, and enterprise?scale machine learning.

You will design and implement scalable ML pipelines, ensure high?quality data flows from ingestion to the Gold Layer, and collaborate across teams to deliver intelligent, data?driven solutions.

Strong communication and business acumen is required as stakeholders/directors/managers/engineers are all onsite, 5-7 years of hands?on ML engineering and data engineering experience, including building and hydrating curated data models and leading technical teams.

Proven ability to design ML?ready data architectures and establish engineering standards, coding practices, and scalable workflows.

Deep understanding of Medallion Architecture, including how to ingest raw source data into Bronze, refine and validate it in Silver, and deliver clean, conformed, analytics? and ML?ready Gold Layer datasets.

Azure Databricks:

Extensive experience using Azure Databricks for ML development, feature engineering, and data engineering pipelines.

Background migrating workloads to Databricks and leveraging Delta Lake, MLflow, and Databricks Workflows to operationalize ML and data transformations.

Python & SQL:

Strong proficiency in Python for model development, feature engineering, and MLOps automation.

Advanced SQL skills to build transformations, views, and optimized ELT pipelines that hydrate the Gold Layer.

Comfortable working in a fast?paced, collaborative environment where experimentation and iteration are encouraged.

Data & Analytics Background:

10+ years in Data & Analytics, delivering enterprise?scale data and ML solutions.

Experience designing feature stores, ML?ready semantic layers, and production?grade data assets.

Ability to integrate ML outputs into analytics tools and business?facing dashboards.

Analytics & Visualization:

Experience designing semantic models and dashboards to surface ML insights, data quality metrics, and model performance.

Familiarity with Power BI best practices, including DAX and visualization standards.