Data Engineering Lead in Charlotte

Energy Jobline ZR; Charlotte (NC); Hybrid; USD 110,000 - 124,000; Full time

Benefits offered by this job

Medical, Dental, and Vision benefits

401k with company matching

Life insurance

Job summary

Energy Jobline ZR is seeking a senior data engineering leader in Charlotte, NC for a hybrid role combining hands-on development with strategic direction. The position requires architecting enterprise-scale data pipelines, governance, and AI-enabled analytics across AWS environments.

The role includes mentoring engineers, influencing architecture, and partnering with cross-functional teams in a W2 arrangement with a solid benefits package.

Qualifications

• Proven leadership of hands-on development with oversight across multiple initiatives.
• Deep experience with AWS environments, infrastructure standards, security controls, and multi-account strategies.
• Track record architecting and governing enterprise data pipelines, ETL, and orchestration.
• History of remediating data gaps and deploying scalable automation for analytics.
• Experience delivering high-reliability pipelines with strong data quality and observability.
• Background in large-scale data warehousing and performance optimization.
• Experience influencing architectural direction and communicating with diverse stakeholders.
• Mentorship and team-building for data engineering teams.
• Cross-functional leadership across engineering and business functions.
• AWS cloud certifications, data governance and metadata platforms, real-time and event-driven architectures, streaming platforms, data mesh and lakehouse patterns, cloud transformation programs, AI application development with AWS Bedrock or similar, AI-ready pipelines and MLOps, proficiency in Python, SQL, Spark, Generative AI, hands-on with AWS, Azure, Databricks, knowledge of LLMs, RAG, vector databases, AI search, and responsible AI, governance, data quality, security, and compliance.

Responsibilities

• Lead hands-on development and provide technical direction across multiple initiatives.
• Establish development environments, infrastructure standards, security controls, and migration strategies across AWS accounts.
• Architect, develop, and govern enterprise-scale data pipelines, ETL processes, ingestion frameworks, and orchestration workflows.
• Identify data gaps, define remediation plans, and implement scalable automation to improve analytics.
• Design reliable data pipelines emphasizing data quality, observability, resiliency, and operational supportability.
• Build and optimize large-scale data warehousing solutions balancing user experience and system performance.
• Drive technical decision-making and communicate complex concepts to stakeholders.
• Mentor engineers and foster a high-performing data engineering culture.
• Lead cross-functional initiatives with data engineering, analytics, architecture, platform engineering, and business stakeholders.

Skills

Leadership

Mentorship

Cross-functional leadership

Data governance

Data quality

Observability

Tools

AWS

Python

SQL

Spark

Databricks

MLOps

LLMs

Job description

Job Description Description

Hybrid 3 days on-site in Charlotte, NC Our client seeks a senior data engineering leader who will drive hands-on development while setting technical direction across multiple initiatives. The role requires expertise in architecting, building, and governing enterprise-scale data pipelines and platforms across cloud environments, with a focus on reliability, quality, observability, and performance. The leader will mentor engineers, influence architectural decisions, and partner with cross-functional stakeholders to advance analytics, governance, and AI-enablement. Due to client requirements, applicants must be willing and able to work on a w2 basis. For our w2 consultants, we offer a great benefits package that includes Medical, Dental, and Vision benefits, 401k with company matching, and life insurance.

Rate: $80.00 to $90.00/hr. w2

JN -102026-109016

Responsibilities

• Lead hands-on development and provide technical direction, code reviews, and engineering oversight across multiple initiatives.
• Establish development environments, infrastructure standards, security controls, and migration strategies across multiple AWS accounts and environments.
• Architect, develop, and govern enterprise-scale data pipelines, ETL processes, ingestion frameworks, and orchestration workflows.
• Identify data gaps, define remediation plans, and implement scalable automation to improve analytical capabilities.
• Design and implement reliable data pipelines emphasizing data quality, observability, resiliency, and operational supportability.
• Build and optimize large-scale data warehousing solutions that balance user experience and system performance.
• Drive technical decision-making and communicate complex concepts to technical and non-technical stakeholders.
• Mentor engineers and foster a high-performing data engineering culture.
• Lead cross-functional initiatives with data engineering, analytics, architecture, platform engineering, and business stakeholders.

Experience Requirements

• Proven leadership of hands-on development with oversight across multiple initiatives.
• Deep experience with AWS environments, infrastructure standards, security controls, and multi-account strategies.
• Track record architecting and governing enterprise data pipelines, ETL, and orchestration.
• History of remediating data gaps and deploying scalable automation for analytics.
• Experience delivering high-reliability pipelines with strong data quality and observability.
• Background in large-scale data warehousing and performance optimization.
• Experience influencing architectural direction and communicating with diverse stakeholders.
• Mentorship and team-building for data engineering teams.
• Cross-functional leadership across engineering and business functions.
• : AWS cloud certifications, data governance and metadata platforms, real-time and event-driven architectures, streaming platforms, data mesh and lakehouse patterns, cloud transformation programs, AI application development with AWS Bedrock or similar, AI-ready pipelines and MLOps, proficiency in Python, SQL, Spark, Generative AI, hands-on with AWS, Azure, Databricks, knowledge of LLMs, RAG, vector databases, AI search, and responsible AI, governance, data quality, security, and compliance.