Lead Data Engineer

Vertage is seeking a Lead Data Engineer to design, architect, and deliver enterprise data engineering solutions across the AWS ecosystem. This hybrid role in the United States focuses on scalable data pipelines, streaming architectures, lake and warehouse platforms, and the operational rigor needed for governance, security, and reliability. The Lead Data Engineer will also modernize data architecture using automation and cloud-native technologies, while providing mentorship and technical direction to engineering teams.

What you will do

• Lead 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 engineering excellence and best practices
• Drive key architectural decisions with Data Architects and Solution Architects to support scalability, security, reliability, and maintainability
• Design and oversee data lake and data warehouse solutions that balance business 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 aligned to 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
• Identify opportunities to modernize data architecture and improve operational efficiency through automation and cloud-native technologies

Experience and technical qualifications

• 8+ years of experience in Data Engineering, including 5+ years working extensively within AWS ecosystems
• Expert-level AWS experience with S3, EMR, Glue Jobs, Lambda, Athena, CloudTrail, SNS, SQS, CloudWatch, and Step Functions
• Experience creating AI applications with AWS Bedrock
• Strong experience implementing enterprise data lake and data warehouse solutions using Lake Formation, Amazon Redshift, and Amazon Athena
• Extensive Kafka-based streaming architecture experience, preferably Confluent Kafka
• Advanced SQL and data modeling expertise, including dimensional modeling and data vault, for large-scale data warehousing
• Deep experience designing, developing, and optimizing scalable, resilient data pipelines in AWS environments
• Strong knowledge 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 hands-on experience using PySpark
• Expertise in Infrastructure as Code using Terraform
• Experience creating 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 pipelines, ETL processes, data ingestion frameworks, and orchestration workflows
• Proven ability to identify data gaps, define remediation plans, and implement scalable automation solutions
• Proven ability to design and implement reliable pipelines with a focus on data quality, observability, resiliency, and operational supportability
• Experience building and optimizing large-scale data warehousing solutions prioritizing business user experience and system performance
• Ability to influence architectural direction and communicate complex technical concepts to technical and non-technical stakeholders
• Track record mentoring engineers 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 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
• English communication with professional working proficiency

Technologies and tools

• AWS, Amazon S3, EMR, Glue Jobs, Lambda, Athena, CloudTrail, SNS, SQS, CloudWatch, Step Functions
• AWS Bedrock, Lake Formation, Amazon Redshift
• Confluent Kafka, Kafka
• SQL, PySpark, Apache Spark, Python
• Terraform, GitHub, GitHub Actions, CI/CD
• AWS IAM
• Apache Airflow
• Generative AI, MLOps, feature engineering, LLMs, RAG, vector databases, Databricks, Azure
• Observability, metadata management, data governance, data quality frameworks

Location, schedule, and compensation

• Location: United States (hybrid); hybrid to Charlotte is preferred
• Hourly rate: USD 80 - 85 per hour
• Start and end date: 12 Oct 2026 – 24 Oct 2027
• Respond by: 10 Oct 2026

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