Top Data Engineer Jobs in Charlotte, NC

Supports cybersecurity identity and access management through data engineering, statistical analysis, machine learning, data pipelines, dashboards, and API integrations. Develops, tests, deploys, monitors, and improves ML-enabled analytical solutions while adhering to security, privacy, model risk, regulatory, and ethical standards. Partners with IAM, technology, business, and risk stakeholders to translate analytical needs into production solutions and communicates recommendations to technical and executive audiences.

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.

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.

Build and operate production-grade ELT pipelines from product, Salesforce, and third-party systems into Snowflake using dbt. Ensure testing, monitoring, schema versioning, governance, privacy, access controls, data quality, cost, and performance. Support AI initiatives with trusted data, contribute to warehouse and semantic-layer architecture, and translate cross-functional stakeholder needs into scalable data solutions.

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.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

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.

Leads the design, engineering, support, and modernization of mission-critical market data platforms, including LSEG TREP and related distribution systems. Builds low-latency, highly available infrastructure; validates feeds; improves resiliency, failover, monitoring, and recovery; troubleshoots data delivery issues; develops UNIX/Linux automation; collaborates on networking, security, upgrades, vendor integration, risk controls, and production support.

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.

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 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.

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.

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.

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.

Support the design, development, and maintenance of data pipelines, workflows, and cloud-based data platforms. Build reliable data assets through ingestion, transformation, profiling, validation, monitoring, and data modeling. Collaborate with cross-functional teams to support analytics, reporting, automation, machine learning, and AI initiatives. Interns will gain experience with Python, SQL, ETL/ELT, Snowflake, cloud technologies, GitHub, and AI tools while receiving mentorship and professional development.

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.

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.

Designs and operates batch, streaming, and real-time data platforms and pipelines using Spark, Kafka, Iceberg, Airflow, and cloud infrastructure. Owns data lake evolution, data quality, observability, reliability, governance, event instrumentation, schema management, and privacy controls for PII, retention, deletion, and residency. Leads complex initiatives end-to-end, troubleshoots production issues, mentors engineers, and develops AI agent harnesses that enforce data engineering standards and quality gates.