"Alerted.org

Description

**Division:** BITS

**Summary Description:** 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.

Academic and Trade Qualifications:

Bachelor's degree in computer science, Computer Information Systems, Computer Engineering, Math, or related technical degree from an accredited institution, and/or equivalent experience.

Work 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.

+ 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.

Key Responsibilities:

Leadership & Strategy (40–50%):

+ 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.

Hands-On Technical Work (50–60%):

+ 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.

Job Knowledge & Technical Expertise:

+ **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).

+ Familiarity with **LLMs** , **Vector Search** , and **Generative AI** integration is preferred.

+ Azure (or equivalent cloud platform) experience strongly preferred.

+ Relevant Databricks, Azure, or ML certifications are a plus.

Skills & Abilities:

+ 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.

Success in the First 6 Months:

+ Establish delivery rhythm and governance for the ML/Data team.

+ Deliver at least one production-grade ML or analytics solution end-to-end on Databricks.

+ Improve team efficiency and platform utilization through process or architecture optimizations.

+ Build strong cross-functional relationships with key stakeholders in engineering, analytics, and business units.

Relationships and Contact:

Work with technical team members to ensure solutions are consistent with development, infrastructure and security guidelines. Collaborate with peers across business lines identifying and documenting user needs and requirements. Keep management informed as to status of projects and activities.

Working Conditions:

Normal business hours, with limited overtime. Local candidates only.

Company Profile:

North Carolina’s Electric Cooperatives is the brand for the family of organizations formed to support the state’s 26 local electric cooperatives, including: North Carolina Electric Membership Corporation, the power supplier to many of the electric cooperatives; North Carolina Association of Electric Cooperatives, the cooperatives’ trade association; and Tarheel Electric Membership Association, Inc. (TEMA), a central purchasing and materials-supply cooperative.

_North Carolina Electric Membership Corporation provides equal employment opportunities (EEO) to all applicants for employment._

Equal Opportunity Employer

This employer is required to notify all applicants of their rights pursuant to federal employment laws.

For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.