Top Data Engineer Jobs in Charlotte, NC

Design, implement, and manage advanced data ecosystems and scalable ETL/ELT pipelines. Analyze complex problems, deliver data warehousing and governance solutions, mentor junior staff, and build client relationships while maintaining high standards and business-context awareness.

Lead design and implementation of data architecture, ETL/ELT pipelines, data models, warehouses and lakes. Ensure data governance, security, cloud deployments, performance and cost optimization. Coach and manage team members, partner with stakeholders, and drive data-driven solutions for clients.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Lead and manage delivery of modern data ecosystems and managed data analytics programs. Oversee project planning, budgeting, quality, and client accounts. Mentor teams, drive data strategy, convert insights to business opportunities, and ensure standards and risk management across large transformations.

Design and implement data architecture and scalable ETL/ELT pipelines on cloud platforms (AWS/Azure/GCP). Analyze complex problems, mentor junior staff, ensure data governance/security compliance, liaise with stakeholders, and deliver high-quality client solutions while developing technical and client-facing skills.

Design and build data infrastructure and pipelines using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, collaborate with clients, and apply data architecture, transformation, and analytics to deliver insights.

Develop and maintain data pipelines and ETL processes, optimize SQL and data architectures, build BI reports and dashboards (Oracle BI, QlikView, Spotfire), analyze data to produce strategic insights, support client engagements in regulated environments, and apply continuous process improvements to ensure data quality and integrity.

As a Principal Data Engineer, you will design and implement LLM, AI-powered security data platforms, mentor engineers, and drive the adoption of data solutions across teams.

As a Data Engineer, you'll lead data engineering projects, develop data transformations, build workflows with Apache Airflow, and ensure data quality and integrity while collaborating with stakeholders.

Design, build, and own scalable data pipelines and evaluation systems that power production AI features and internal analytics. Ensure data quality across ingestion, modeling, and reporting, collaborate with ML and analytics teams, deploy and monitor ML systems, and establish standards for data work and evaluation.

Design, build and maintain scalable data pipelines and automated workflows using Python, Airflow, DBT and cloud warehouses. Partner with Analytics, Sales and Marketing to deliver curated marketing datasets, enforce data quality through validation and CI/CD, and support production deployment and monitoring of Enterprise Data Lake/ODS solutions.

Build, model, and maintain scalable BigQuery-based data solutions on GCP. Implement performant data models, storage partitioning/clustering, ETL improvements, data validation, and documentation. Collaborate with architects, data scientists, and engineers to deliver governed, high-quality data for reporting and AI initiatives.

Lead design and modernization of scalable, secure data systems and real-time pipelines for xOT cybersecurity. Create data contracts, observability, lineage, and cross-functional solutions supporting cloud and on-prem deployments, Kubernetes, and stream processing to enable threat detection and intelligence.

Design, author, test, and tune enterprise DLP rules and policies across email, endpoints, web, and SaaS. Maintain runbooks, exception workflows, user guidance, and release notes. Use telemetry and controlled experiments to reduce false positives, measure precision/recall, and report efficacy. Provide technical support, collaborate with legal, incident response, and platform teams, and apply AI-assisted tools responsibly in development and production.

As a Senior AI/Data Engineer, you will design and maintain scalable data and AI infrastructures, partnering with cross-functional teams to deliver reliable data systems and support AI-driven business capabilities.

The Senior Data Engineer will design, build, and operate scalable data pipelines for high-performance racing programs, utilizing cloud architecture and streaming systems to support real-time data delivery.

Lead design, build, and operate scalable, secure GCP data platforms and reusable frameworks (ingestion, transformation, quality, orchestration). Enable self-service consumption and governance-by-design, optimize performance and cost, implement data modeling and observability, enforce security controls, partner with domain teams to onboard use cases, and contribute to platform roadmap and enablement.

Design, build, and maintain production-grade ETL/ELT pipelines and data models using dbt, Spark, and cloud platforms. Manage ingestion, data quality, observability, semantic layers, and BI outputs. Collaborate with analysts and data scientists to deliver governed, high-quality data products and implement DataOps and CI/CD practices.

Build, test, and maintain dbt/SQL transformations and pipelines in Snowflake; assist with dimensional modeling, data quality, documentation, and governance; collaborate with analysts and business users; use Git workflows; apply analytical problem-solving; learn observability and AI-assisted engineering under mentorship.

Lead development and delivery of healthcare AI/GenAI solutions for health systems—architect RAG and agentic workflows, manage global MLOps and model governance, engage executive stakeholders, and drive production-grade use cases across clinical, financial, and operational domains.

Lead development and delivery of data engineering solutions for clients: design data architectures, build and maintain ETL pipelines, apply advanced analytics, implement BI visualizations, coach teams, manage managed-services SLAs, and engage stakeholders to validate and refine outcomes.

Lead data engineering engagements delivering ETL/ELT solutions, pipelines, and cloud analytics. Build and maintain architectures (DataStage, AWS/Redshift, DB2/SQL Server, GoldenGate/CDC, S3/Glue), oversee BI and visualization, manage client accounts and teams, mentor junior staff, ensure project delivery and quality, and drive process and technology adoption.

Design, build, and operate cloud-native, domain-oriented data products and pipelines across Azure and AWS using Databricks and Snowflake. Lead migration from legacy platforms, implement batch and streaming processing with Spark, ensure security and governance compliance, promote best practices (CI/CD, testing, observability), and mentor junior engineers while collaborating with product, analytics, and security teams.

Lead design and delivery of scalable, secure enterprise data engineering solutions. Set architecture and standards, mentor teams, implement cloud data platforms, data modeling, governance, quality controls, CI/CD, and liaise with executives and stakeholders to support analytics, compliance, and customer initiatives.

Design, build, test, and support scalable data pipelines and reporting solutions for AML. Integrate, transform, and validate data, monitor production systems, troubleshoot issues, and collaborate with product, engineering, and business teams to ensure data quality, performance, and regulatory compliance.