Lead Data Engineer

E4E RELIEF LLC•Charlotte, NC

just now•Hybrid

About The Position

Job Summary: The Lead Data Engineer is a hands-on Data Engineer that will own the design, development, and governance of our data platforms. Leveraging Azure, with primary focus on Microsoft SQL Server, Power BI reporting, and cloud-based data services. This role partners with analytics, product, and engineering teams to deliver scalable data solutions that offer best-in-class reporting to our customers while enabling data-driven decision making across the organization Supervisory/Functional Leadership Responsibilities: Foster high-performing and supportive team environment. Essential Responsibilities: Reasonable accommodations may be made to enable individuals with disabilities to perform these essential functions. Data architecture and governance Design and maintain scalable data models on Microsoft SQL Server and Azure data services, ensuring data quality, lineage, security, and compliance. Define and implement data governance standards, metadata management, and data retention policies. Champion data quality initiatives, profiling, and automated validation across the data pipeline. Data engineering and platform development Build, optimize, and maintain ETL pipelines, preferably using SQL Server SSIS Lead migration and modernization efforts from on-premises SQL Server to cloud-native architectures, including data lake patterns where appropriate. Implement scalable data ingestion from multiple sources, ensuring low-latency, reliable data delivery to analytics consumers. BI and analytics enablement Architect and govern data models, datasets, security, and performance optimizations; partner with analysts to deliver self-serve analytics capabilities, preferably leveraging Power BI Create reusable data services and dashboards that enable senior leadership to monitor KPIs and metrics with accuracy and speed. Cloud engineering and operations Own Azure environment design, cost optimization, security controls (RBAC, data encryption, threat detection), and disaster recovery planning as it relates to the data domain Establish CI/CD pipelines for data assets and analytics artifacts; promote best practices for versioning, testing, and deployment. Leadership and collaboration Mentor data engineers, establish coding standards, code reviews, and best-practice documentation. Collaborate with product managers and business stakeholders to translate requirements into scalable data solutions. Manage project timelines, risk, and stakeholder communication, balance delivery speed with data quality and reliability. Work with the data or engineering teams to perform root cause analysis of field reported defects Report defects in defect tracking system by providing detailed documentation as proof of test failures for repeatability. Compare testing metrics to exit criteria to provide QA advice on product release. Other duties: Performs other related duties as assigned. Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities, and activities may change at any time with or without notice.

Requirements

6+ years of experience in data engineering or a closely related role, with a track record delivering enterprise-grade data platforms
Deep expertise in Microsoft SQL Server (design, tuning, indexing, query optimization) and Azure data services (Azure SQL, Data Factory, Synapse, Data Lake, storage, identity and security features).
Strong Power BI proficiency (data modeling, DAX, performance optimization, security roles, and report deployment).
Experience migrating or modernizing data architectures to cloud environments; ability to design scalable, robust, and cost-conscious solutions.
Proven ability to lead technical teams, mentor engineers, and drive cross-functional collaboration.
Knowledge of data governance, data quality frameworks, and data security/compliance considerations.
Excellent communication skills, with the ability to translate technical concepts to non-technical stakeholders.
Prolonged periods sitting at a desk and working on a computer.
Client-facing interactions both virtual and in-person.
Ability to travel to client engagement sessions/conferences.
Must be able to lift to 10 pounds at times.

Responsibilities

Data architecture and governance
Design and maintain scalable data models on Microsoft SQL Server and Azure data services, ensuring data quality, lineage, security, and compliance.
Define and implement data governance standards, metadata management, and data retention policies.
Champion data quality initiatives, profiling, and automated validation across the data pipeline.
Data engineering and platform development
Build, optimize, and maintain ETL pipelines, preferably using SQL Server SSIS
Lead migration and modernization efforts from on-premises SQL Server to cloud-native architectures, including data lake patterns where appropriate.
Implement scalable data ingestion from multiple sources, ensuring low-latency, reliable data delivery to analytics consumers.
BI and analytics enablement
Architect and govern data models, datasets, security, and performance optimizations; partner with analysts to deliver self-serve analytics capabilities, preferably leveraging Power BI
Create reusable data services and dashboards that enable senior leadership to monitor KPIs and metrics with accuracy and speed.
Cloud engineering and operations
Own Azure environment design, cost optimization, security controls (RBAC, data encryption, threat detection), and disaster recovery planning as it relates to the data domain
Establish CI/CD pipelines for data assets and analytics artifacts; promote best practices for versioning, testing, and deployment.
Leadership and collaboration
Mentor data engineers, establish coding standards, code reviews, and best-practice documentation.
Collaborate with product managers and business stakeholders to translate requirements into scalable data solutions.
Manage project timelines, risk, and stakeholder communication, balance delivery speed with data quality and reliability.
Work with the data or engineering teams to perform root cause analysis of field reported defects
Report defects in defect tracking system by providing detailed documentation as proof of test failures for repeatability.
Compare testing metrics to exit criteria to provide QA advice on product release.
Performs other related duties as assigned.