Top Data Engineer Jobs in Charlotte, NC

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.

Develop and implement data solutions, pipelines, architectures, analytics, reports, and dashboards for clients. Apply Python, Java, ETL, business intelligence, and advanced analytics techniques to interpret complex data, improve operations, and support strategic decisions. Collaborate with stakeholders, manage client relationships, identify data challenges, and deliver high-quality managed services work while contributing to continuous process improvement.

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.

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 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.

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.

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.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

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.

Configure and maintain Adobe Experience Platform and Real-Time CDP data pipelines, XDM schemas, datasets, identity resolution, Profile enablement, segmentation readiness, and destination activation. Validate data quality, troubleshoot ingestion and identity issues, manage sandboxes, and implement privacy, consent, governance, and access controls. Partner with architects, data engineers, consultants, analysts, data scientists, and marketing teams to support reporting, personalization, and machine learning readiness.

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.

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, build, and operate production AI and data systems for marketing: LLM/agent orchestration, retrieval pipelines, data pipelines, CDP and warehouse architecture, automation of manual workflows, data quality and reliability, stakeholder collaboration, and mentoring. Ship production-grade Python and SQL solutions and evaluate/monitor AI systems.

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.

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.

Design, build, and maintain optimized, highly available data pipelines and the BMG Data Warehouse. Lead medium-to-complex data integration, data modeling, code reviews, QA, and cloud migration efforts. Collaborate with business partners to translate requirements into solutions, resolve technical issues, and support analytics and reporting needs while adhering to policies and compliance.

Lead design, development, and support of scalable data solutions for counterparty credit risk. Build and maintain data pipelines, reports, and dashboards; collaborate with business stakeholders; resolve technical challenges; lead projects and mentor junior staff; ensure compliance with risk and regulatory requirements.

Leads data center infrastructure service delivery, lifecycle management, engineering readiness, standardized provisioning, and implementation processes. Designs repeatable, automation-ready workflows using ServiceNow and supporting tools, translating business priorities into executable plans. Advises leadership on complex enterprise technology solutions across infrastructure, applications, networks, security, databases, operating systems, and web technologies. Drives innovation, risk management, operational efficiency, and resolution of large-scale technical challenges.

Design, build, and operate Lowe's scalable Search Platform using OpenSearch/Elasticsearch and GCP. Develop batch and near-real-time ingestion/ETL pipelines, cloud-native services, APIs, and CI/CD. Improve performance, reliability, monitoring, and mentor engineers while collaborating across teams.

Designs and operates scalable GCP data pipelines, reusable engineering frameworks, feature stores, orchestration workflows, and governed data models supporting AI and machine learning. Builds Python, SQL, and Beam transformation libraries; ensures workload cost, performance, reliability, quality, lineage, and observability; and partners with data scientists migrating models to GCP. The role also documents best practices, provides enablement, and supports production systems during off-hours.

Leads large-scale data engineering initiatives, including migrating SQL Server implementations to Databricks. Designs, codes, tests, documents, and reviews complex technology solutions while establishing engineering standards, guiding teams, resolving technical issues, and mentoring peers. The role supports advanced analytics, data governance, data onboarding, cloud technologies, and modern data architecture.

Design and build scalable cloud data platforms, pipelines, and data products. Improve data quality, governance, observability, and performance. Leverage AI-augmented engineering, lead technical initiatives, mentor engineers, and collaborate with stakeholders to deliver analytics and ML-ready solutions.

Design, build, and maintain Python-based data applications and scalable data pipelines using AWS Glue and PySpark; deploy containerized services on EKS; implement APIs with API Gateway and Lambda; integrate PostgreSQL, DynamoDB, and Kafka; follow TDD, CI/CD, and document work in Confluence and JIRA while collaborating in Agile teams.

Design, build, deploy, and operate production AI, ML, and GenAI solutions for enterprise lending platforms. Develop Python data pipelines, APIs, microservices, and ML lifecycle processes covering preparation, training, validation, monitoring, and retraining. Apply MLOps, CI/CD, containerization, testing, data governance, security, compliance, and model risk controls while collaborating with product, operations, engineering, and business stakeholders.