Top Data Engineer Jobs in Charlotte, NC

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, 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.

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.

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 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.

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.

Lead design and delivery of a next-generation workplace data platform—own architecture, enable advanced analytics/AI, drive data governance, mentor engineers, and advise leadership on large-scale data solutions.

Design, build, and operate scalable real-time and batch data pipelines ingesting high-frequency telemetry, simulation, and trackside data. Own Kafka/Flink streaming components, Databricks lakehouse implementations, and infrastructure-as-code deployments. Develop software in Python/Java/SQL, integrate systems, automate testing and releases, performance-tune pipelines, and mentor peers to ensure resilient, secure, high-performance data delivery for motorsports teams.

Lead engineering, design, and support of low-latency, high-availability market data platforms (LSEG TREP). Build and automate UNIX/Linux infrastructure, manage feed handlers, ensure resilient multicast/unicast distribution, resolve latency/feed issues, collaborate with networks/vendors, drive modernization, and maintain compliance and operational controls.

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 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.

Design, build, and maintain cloud-based data pipelines and architectures on AWS, Azure, or GCP. Advise clients on cloud data solutions, implement production data products, collaborate with stakeholders, and evaluate architectural trade-offs to meet business needs.

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 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.

Design, build, and maintain secure data pipelines for structured and unstructured sources. Segment and protect data domains, enforce access controls and classification, and enable analytics/AI use cases. Optimize workflows for performance, observability, and resiliency across cloud and hybrid platforms. Partner with stakeholders to translate compliance and business needs into reusable, governed data solutions and support production systems with strong attention to integrity, lineage, and auditability.

Design and maintain data pipelines, data contracts, and transformation layers for analytics. Model complex sources, enable observability and lineage, collaborate with product teams, and optimize data structures for scalable cybersecurity and industrial datasets.

Design and scale data pipelines and ML/LLM systems, build agentic automation for pipeline generation and maintenance, improve data monitoring, and collaborate with analysts, product, and ML teams to deliver reliable end-to-end data and AI infrastructure for a high-growth e-commerce platform.

The Data Engineer will develop data pipelines, support Agile teams, ensure data performance, and collaborate to implement complex requirements for federal customers.

As a Data Engineer II, you will design and support ETL/ELT data pipelines, work with AWS, Snowflake, and collaborate within an Agile team on data initiatives.

Lead design, development, and modernization of enterprise data platforms and cloud-native analytics solutions. Provide architecture guidance, mentor engineers, build scalable ETL/ELT pipelines, enable data governance, CI/CD/DevSecOps, and optimize performance across data lakes, warehouses, and analytics ecosystems while supporting client-facing technical efforts.