Machine Learning /Data Engineer

We are seeking a Senior ML Engineer with strong data engineering foundations and deep experience building MLready 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 enterprisescale machine learning.

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

Key Responsibilities: Lead the development and maturity of the Modern ML & Data Platform.

Architect and maintain endtoend ML pipelines, including data ingestion, feature engineering, model training, deployment, and monitoring.

Design and operationalize Bronze Silver Gold data flows that support ML workloads and enterprise analytics.

Build and maintain the Enterprise Feature Store and MLready Gold Layer datasets.

Translate business requirements into scalable ML and data engineering solutions.

Implement robust ETL/ELT processes, data acquisition strategies, and automated workflows.

Collaborate with data engineering, analytics, and product teams to ensure ML solutions meet business needs.

Mentor team members and enforce engineering best practices, coding standards, and model governance.

Ensure data governance, security, lineage, and performance optimization across ML and data pipelines.

Support the development of dashboards and semantic models that surface ML insights and operational metrics.

Dimensions Required: Selfstarter

5 7 years of handson ML engineering and data engineering experience, including building and hydrating curated data models and leading technical teams.

Proven ability to design MLready 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 MLready 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 fastpaced, collaborative environment where experimentation and iteration are encouraged.

Data & Analytics Background: 10+ years in Data & Analytics, delivering enterprisescale data and ML solutions.

Experience designing feature stores, MLready semantic layers, and productiongrade data assets.

Ability to integrate ML outputs into analytics tools and businessfacing 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.

Secondary Skills - Nice to Haves

• Statistical model
• Tensorflow
• Big data, Problem Analysis & Judgment

Decisiveness & Risk Taking

Planning & Organization

Delegation & FollowUp

Communication & Persuasiveness

Adaptability & Drive

Continuous Learning & SelfDevelopment

Additional Skills & Qualifications

Additional Skills (Nice to Have) Experience with GIS

Familiarity with LLMs, generative AI, and advanced ML techniques.

Exposure to model monitoring, drift detection, and ML observability tools.

Employee Value Proposition (EVP)