Aws Lakehouse Developer Jobs

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Lead Data Engineer

Vertage United States

United States

Today

$80 - $85/hr Full time Amazon Web Services AWS Aws Glue

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Job Description

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

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