Manager, ML Operations & Data Engineering

North Carolina's Electric Cooperatives•Raleigh, NC

About The Position

The Lead ML & Data Engineering Manager will oversee and actively contribute to the full machine learning and data engineering lifecycle — from data ingestion and feature engineering through model development, deployment, monitoring, and continuous improvement — within a cloud-native Databricks Lakehouse environment . This role combines hands-on technical execution with team leadership and strategic alignment . The individual will manage and mentor a cross-functional data team (ML engineers, data engineers, and analysts), ensuring high-quality delivery, platform optimization, and adherence to governance and security standards. The Lead will also make architectural and process recommendations based on industry best practices, balancing innovation with operational excellence. They will be accountable for strengthening system controls, improving efficiency through automation, and guiding the evolution of our AI and data ecosystem for scalability and sustainability.

Requirements

Bachelor's degree in computer science, Computer Information Systems, Computer Engineering, Math, or related technical degree from an accredited institution, and/or equivalent experience.
5–10 years of progressive experience in data, machine learning, or software engineering roles, with a proven track record of delivering production-grade ML and data solutions.
At least 3 years of hands-on experience designing, developing, optimizing, and deploying machine learning models in production environments (preferably using Databricks, Azure ML, or similar platforms).
2+ years of leadership experience as a technical lead, team lead, or manager overseeing data engineers, ML engineers, or data scientists — including mentoring, code review, and project delivery oversight.
Demonstrated experience integrating ML models into operational systems, APIs, or business workflows.
Background in data architecture, pipeline orchestration, and performance optimization across large datasets.
Databricks platform experience required — including Lakehouse architecture, cluster management, Delta tables, and Spark.
Proficiency with MLflow , Feature Store , and AutoML workflows.
Strong foundation in Python , SQL , and ML frameworks such as scikit-learn , PyTorch , TensorFlow , or XGBoost .
Experience with CI/CD , Git-based workflows , and DevOps principles for ML (MLOps).
Azure (or equivalent cloud platform) experience strongly preferred.
Proven ability to lead and mentor technical teams while remaining a hands-on contributor.
Deep understanding of MLOps best practices : model lifecycle management, observability, and retraining automation.
Strong experience in data preparation , feature engineering , and exploratory data analysis .
Ability to translate business requirements into scalable technical solutions .
Excellent written and verbal communication; able to interface confidently with both technical and non-technical audiences.
Demonstrated ability to work independently, manage multiple priorities, and deliver under tight deadlines.
Familiarity with Agile and iterative development methodologies.

Nice To Haves

Experience within the public utility, energy, or infrastructure sector is highly desirable, particularly with applications such as load forecasting, outage prediction, grid optimization, or asset analytics.
Proven ability to collaborate cross-functionally with data platform, analytics, and business teams to translate organizational goals into scalable data and ML solutions.
Familiarity with LLMs , Vector Search , and Generative AI integration is preferred.
Relevant Databricks, Azure, or ML certifications are a plus.

Responsibilities

Lead, mentor, and develop a cross-functional team of ML engineers, data engineers, and analysts.
Translate business needs into actionable data and ML initiatives with clear milestones and measurable outcomes.
Define and enforce team processes, standards, and best practices for data engineering, model development, and deployment.
Manage sprint planning, prioritization, and delivery for ML and data projects.
Collaborate closely with the Director of Data Engineering to align technical strategy with enterprise data governance, architecture, and security policies.
Champion innovation by staying current with trends in AI, ML, and data infrastructure, identifying opportunities for continuous improvement.
Design, develop, and deploy scalable, production-ready machine learning models and data pipelines.
Optimize workloads for cost, performance, and reliability within the Databricks Lakehouse ecosystem.
Build and maintain feature pipelines, MLflow model registries, and CI/CD workflows for automated training and deployment.
Process, transform, and analyze large-scale structured and unstructured datasets.
Integrate models into APIs, applications, or downstream systems (e.g., Azure Container Apps, Model Serving Endpoints).
Ensure compliance with data governance, lineage, and security standards.
Conduct code reviews, provide technical mentorship, and contribute to architecture design decisions.