Aws Lakehouse

Aws Lakehouse Engineer Jobs

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Today

Lead Data Engineer

Bartech Staffing Charlotte, NC

Charlotte, NC

Today

Full time Backend Developer Manager Amazon Web Services

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Job Description

Hybrid to Charlotte, NC preferred. Bartech Staffing seeks a Lead Data Engineer to own the design, architecture, and implementation of enterprise data engineering solutions across the AWS ecosystem. In this role, you will provide technical leadership, guide engineering teams through best practices, and help modernize data platforms with an emphasis on reliability, security, and long-term maintainability.

Responsibilities

Lead the design, architecture, and implementation of enterprise data engineering solutions across the AWS ecosystem
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 warehouse and data lake solutions that balance usability, performance, and long-term sustainability
Set engineering standards for data modeling, ETL frameworks, pipeline reliability, monitoring, and operational excellence
Lead end-to-end delivery to ensure 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 initiatives
Champion data quality, governance, observability, and data stewardship practices across platforms and teams
Identify modernization opportunities to improve operational efficiency through automation and cloud-native technologies

Requirements

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
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 skills, including dimensional modeling and data vault, for large-scale data warehousing
Deep experience building, developing, and optimizing scalable and resilient data pipelines within AWS environments
Strong knowledge of distributed 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 PySpark experience
Expertise in Infrastructure as Code using Terraform
Experience implementing CI/CD frameworks using GitHub and GitHub Actions
Deep understanding 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, data ingestion frameworks, and orchestration workflows
Proven ability to identify data gaps, define remediation plans, and implement scalable automation that improves analytical capabilities
Ability to build highly reliable data pipelines with a strong focus on data quality, observability, resiliency, and operational supportability
Experience building and optimizing large-scale data warehousing solutions that prioritize business user experience and system performance
Ability to influence architectural direction and communicate complex technical 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 in fast-paced enterprise environments with competing priorities
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

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

Role Summary / Purpose

Lead Data Engineer – Hybrid to Charlotte preferred

Nice-to-have / Preferred Skills

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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13d ago

Lead Data Engineer

Vertage United States

United States

13d ago

$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, and secure, well-governed data lake and data warehouse platforms, with ongoing operational excellence.

Role focus

You will provide technical leadership for end-to-end delivery, covering architectural decisions, production support, data governance, and modernization efforts. The role emphasizes reliability, observability, and data quality across AWS-based platforms, alongside the development of AI-ready data pipelines.

Key responsibilities

Lead the design, architecture, and implementation of enterprise data engineering solutions across AWS
Collaborate 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 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 aligned to business requirements, enterprise architecture standards, and regulatory requirements
Oversee production support and operational management of AWS-based data platforms, performing root-cause analysis and performance optimization
Champion data quality, governance, observability, and data stewardship across platforms and teams
Identify modernization opportunities to improve operational efficiency using automation and cloud-native technologies

Experience and requirements

8+ years of Data Engineering experience, including 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 and data vault for large-scale warehousing
Deep experience building and optimizing scalable, resilient data pipelines within AWS, including distributed processing (notably PySpark and EMR)
Advanced Python development 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
Experience with workflow orchestration tools such as AWS Step Functions or Apache Airflow (or equivalents)
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 with 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
Proven ability to identify data gaps, define remediation plans, and implement scalable automation that improves organizational analytics
Experience building and optimizing large-scale data warehousing solutions with strong business usability and system performance
Ability to influence architectural direction and communicate complex technical concepts to technical and non-technical stakeholders
Proven track record mentoring engineers 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 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
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

Other details

Location: United States (hybrid); hybrid to Charlotte is preferred
Hourly rate: USD 80–85 per hour
Start and end dates: 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 with 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
Indicative technologies: 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, Terraform, GitHub, GitHub Actions, AWS IAM, Apache Airflow, Python, and related data governance, observability, and AI/data platform capabilities

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