Lead Data Engineer

Bartech Staffing is hiring a Lead Data Engineer for a hybrid role based in Charlotte, North Carolina, with Charlotte preferred. In this position, you will lead the design and delivery of enterprise data engineering solutions across the AWS ecosystem, providing technical direction and hands-on mentorship to build reliable, scalable platforms.

What You Will Do

• Own the design, architecture, and implementation of enterprise data engineering solutions across AWS
• Partner with Lead Developers, Data Scientists, Architects, Product Owners, and business stakeholders to define technical strategy and scalable solutions
• Provide technical leadership and mentorship to Data Engineers and development teams, promoting best practices and engineering excellence
• Drive key architectural decisions with Data Architects and Solution Architects to support scalability, security, reliability, and maintainability
• Design and oversee data warehouse and data lake solutions that balance usability, performance, and long-term sustainability
• Establish engineering standards for data modeling, ETL frameworks, pipeline reliability, monitoring, and operational excellence
• Lead end-to-end solution delivery, ensuring alignment with business requirements, enterprise architecture standards, and regulatory requirements
• Oversee production support and operational management of AWS-based data platforms, including root-cause analysis and performance optimization
• Champion data quality, governance, observability, and data stewardship practices across platforms and teams
• Find opportunities to modernize data architecture and improve operational efficiency through automation and cloud-native technologies

Core Requirements

• 8+ years of Data Engineering experience, including 5+ years working extensively within AWS ecosystems
• Expert knowledge of AWS services including S3, EMR, Glue Jobs, Lambda, Athena, CloudTrail, SNS, SQS, CloudWatch, and Step Functions
• Experience creating AI applications with AWS Bedrock
• Strong experience building enterprise-scale data lake and data warehouse solutions using Lake Formation, Amazon Redshift, and Amazon Athena
• Extensive experience with Kafka-based streaming architectures, preferably Confluent Kafka
• Advanced SQL and data modeling expertise, including dimensional modeling and data vault approaches for large-scale warehousing
• Deep experience designing, developing, and optimizing scalable, resilient data pipelines in AWS environments
• Strong understanding of distributed data processing frameworks, particularly PySpark and EMR
• Expertise in database management principles, performance tuning, and data architecture best practices
• Advanced Python development skills with hands-on experience using PySpark
• Expertise in Infrastructure as Code using Terraform
• Experience designing and implementing CI/CD frameworks using GitHub and GitHub Actions
• Deep knowledge of AWS IAM roles, policies, governance, and security best practices
• Experience with workflow orchestration tools such as AWS Step Functions, Apache Airflow, or equivalent platforms
• Experience leading cloud migration, modernization, and enterprise data platform initiatives
• Strong understanding of data governance, metadata management, data quality frameworks, and observability principles
• Ability to lead hands-on development while providing technical direction, code reviews, and engineering oversight across multiple initiatives
• Experience establishing development environments, infrastructure standards, security controls, and migration strategies across multiple AWS accounts and environments
• Ability to architect, develop, and govern enterprise-scale data pipelines, ETL processes, ingestion frameworks, and orchestration workflows
• Proven ability to identify data gaps, define strategic remediation plans, and implement scalable automation solutions that improve analytical capabilities
• Ability to design and implement highly reliable data pipelines with strong focus on data quality, observability, resiliency, and operational supportability
• Experience building and optimizing large-scale data warehousing solutions with attention to business user experience and system performance
• Ability to influence architectural direction, drive technical decisions, and communicate complex concepts to technical and non-technical stakeholders
• Proven track record mentoring engineers, fostering technical growth, and building high-performing data engineering teams
• Experience leading cross-functional initiatives across data engineering, analytics, architecture, platform engineering, and business stakeholders
• Strong problem-solving and leadership skills, with the ability to manage competing priorities in fast-paced enterprise environments
• Experience building AI-ready data pipelines and ML workflows, including feature engineering and MLOps
• Proficiency in Python, SQL, Spark, and Generative AI technologies for scalable data and analytics solutions
• Hands-on experience with AWS, Azure, or Databricks and cloud-based AI/data platforms
• Knowledge of LLMs, RAG, and vector databases to support AI-powered applications and intelligent search
• Strong understanding of data governance, quality, security, and responsible AI practices

Technologies You Will Work With

• AWS, S3, EMR, Glue Jobs, Lambda, Athena, CloudTrail, SNS, SQS, CloudWatch, Step Functions
• AWS Bedrock, Lake Formation, Amazon Redshift, Kafka, Confluent Kafka
• SQL, dimensional modeling, data vault, PySpark, Python, Terraform
• GitHub, GitHub Actions, AWS IAM, Apache Airflow, ETL, CI/CD
• Generative AI, Azure, Databricks, LLMs, RAG, vector databases, MLOps, Spark

Preferred Qualifications

• AWS Certified Data Engineer, AWS Solutions Architect, or equivalent cloud certifications
• Experience implementing enterprise data governance and metadata management platforms
• Experience with real-time analytics, event-driven architectures, and streaming data platforms
• Knowledge of modern data architecture patterns including Data Mesh, Lakehouse, and domain-oriented design
• Experience leading large-scale cloud transformation or enterprise data modernization programs