Top Data Engineer Jobs in Charlotte, NC

Leads enterprise software engineering initiatives supporting financial forecasting, planning, and reporting. Designs, codes, tests, documents, and supports Oracle Essbase 21c solutions and associated infrastructure. Develops ETL solutions using Python, PySpark, and object storage; manages platform capacity, availability, performance, security, middleware, and production issue resolution. Establishes engineering standards, mentors teams, resolves complex technical issues, and supports modernization toward cloud-based data engineering and analytics platforms.

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.

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.

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.

Build and operate AWS GovCloud data infrastructure across development, preproduction, and production environments. Establish PostgreSQL platforms, infrastructure-as-code with Chef, BI infrastructure, identity integrations, monitoring, observability, CI/CD, and disaster recovery. Support data pipelines, troubleshoot incidents, optimize databases, and ensure FedRAMP and FISMA compliance. Collaborate with security, compliance, DevOps, and data engineering teams while delivering greenfield infrastructure from architecture through production.

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.

Designs and maintains enterprise ETL and data integration pipelines, develops scalable data warehouse solutions, optimizes SQL and data workflows, and supports migration of legacy platforms to Google Cloud. The role focuses on data quality, performance, reliability, troubleshooting, and collaboration with technical and business stakeholders.

Design, build, and maintain scalable data pipelines and platforms for banking data. Collaborate with teams to ensure secure, reliable data products.

As the first in-house Data Engineer, you will own the data infrastructure, build and maintain data pipelines, and collaborate with teams to ensure quality data access and insights.

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.