Lead Data Engineer

Role summary / purpose Lead Data Engineer – Hybrid to Charlotte preferred Required skills • 8+ years of experience in Data Engineering with at least 5+ years working extensively within AWS ecosystems • Expert-level experience with 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 designing and implementing 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, data vault, and large-scale data warehousing solutions • Deep experience designing, developing, and optimizing scalable, resilient data pipelines within AWS environments • Strong understanding of distributed data processing frameworks, particularly PySpark and EMR • Expert knowledge of database management principles, performance tuning, and data architecture best practices • Advanced Python development skills with extensive 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 • Strong 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 efforts 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, data ingestion frameworks, and orchestration workflows • Proven ability to identify data gaps, define strategic remediation plans, and implement scalable automation solutions that improve analytical capabilities across the organization • 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 that prioritize both business user experience and system performance • Ability to drive technical decision-making, influence architectural direction, and effectively communicate complex technical concepts to both 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 involving 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…