Top Data Engineer Jobs in Charlotte, NC

Build and maintain reliable data pipelines, computed tables, and scalable data models using SparkSQL and PySpark. Integrate IoT, product, customer, external, unstructured, and ML-related data into a central data lake for analytics, causal inference, model training, and dashboards. Ensure high data quality and uptime while collaborating with data science, analytics, AI/ML, and engineering teams.

Leads enterprise architecture and engineering standards for public-cloud data, analytics, AI/ML, and Agentic AI platforms. Responsibilities include modernizing legacy platforms, designing lakehouse and real-time ecosystems, establishing governance and reliability standards, enabling self-service data products, and developing AI-agent automation. The role partners with security, risk, compliance, and engineering teams to deliver secure, resilient, observable, and cost-efficient cloud capabilities in a regulated enterprise environment.

Build and support scalable data pipelines, ETL/ELT processes, orchestration workflows, APIs, and cloud data services on Google Cloud Platform. Integrate enterprise platforms with governance systems for metadata discovery, lineage, classification, and policy enforcement. Develop secure, observable, cost-efficient solutions using CI/CD, testing, automation, DataOps, and DevSecOps practices, while contributing to AI-enabled workflows and reusable platform frameworks.

Lead the design, development, and operation of reusable cloud-native data platforms on GCP. Build standardized batch and streaming frameworks, ingestion pipelines, transformation libraries, orchestration capabilities, data models, governance controls, and observability solutions. Optimize workload cost, performance, security, and reliability while partnering with engineering, analytics, and ML teams. Provide documentation, enablement, platform roadmap contributions, and occasional off-hours production support.

Leads global teams developing production AI, GenAI, RAG, agentic workflow, and MLOps solutions for health systems and health plans. Translates clinical, financial, and operational challenges into HIPAA-compliant use cases involving clinical decision support, population health, revenue cycle, research, and optimization. Oversees healthcare data science engagements, model governance, executive stakeholder communications, client delivery, and team development.

Leads the modernization of enterprise data platforms supporting reporting, statements, and letters. Architects cloud-native data platforms, ETL/ELT pipelines, streaming solutions, and real-time processing using AWS, Snowflake, Python, dbt, Airflow, and Kafka. Establishes engineering standards, technical roadmaps, observability, data quality, CI/CD, and DevOps practices. Provides hands-on technical leadership, mentors engineers, manages delivery across internal and vendor teams, resolves production issues, and drives cloud transformation and platform reliability.

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Own data pipelines into and out of the warehouse, dbt models, data integrity, governance, identity resolution, reverse ETL, and quality monitoring across growth, lifecycle, and brokerage data. Partner with product, marketing, and operations teams to establish data contracts, ensure accurate and timely business metrics, and support regulated financial data requirements.

Designs, builds, and maintains cloud data pipelines, storage systems, and reusable infrastructure for data and analytics initiatives. Develops scalable ETL processes using cloud big data tools, supports feature engineering and machine learning deployment, and builds AI-integrated applications. The role also involves data modeling, governance, cybersecurity compliance, process improvement, technical project leadership, mentoring, and delivering enterprise data assets in a regulated financial services environment.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, enforcing security and compliance, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires extensive infrastructure and data engineering experience across cloud platforms, containers, data security, and workflow orchestration.

Serves as the senior technical authority for enterprise browser data protection and security. Owns architecture, roadmap, standards, lifecycle, browser-based DLP controls, integrations, automation, and operational support. Leads complex troubleshooting, major incident response, root-cause analysis, runbook development, and continuous improvement. Partners across cybersecurity, identity, endpoint, network, cloud, risk, compliance, and vendor teams while mentoring engineers and influencing enterprise security design.

Build a high-performance frontend data platform for exploring and analyzing hardware telemetry. Develop React and TypeScript applications, interactive time-series visualizations, data visualization plugins, complex application state management, and LLM-assisted analysis workflows. Collaborate directly with hardware engineers and customers to define interfaces, prototype workflows, demonstrate features, and incorporate feedback into product development.

Staff Data Engineer responsible for setting technical direction, architecting scalable data platforms, leading cross-functional initiatives, improving reliability and performance, and enabling AI-assisted analytics and development. The role includes hands-on development with Python, SQL, cloud data warehouses, orchestration, infrastructure as code, Kubernetes, Kafka, and streaming pipelines, along with mentoring, technical advocacy, documentation, code reviews, and on-call troubleshooting.

Staff Data Engineer leading architecture and technical direction for real-time and batch motorsports data platforms. Responsibilities include telemetry ingestion, streaming systems, data quality, observability, cloud platforms, reliability, incident response, technical standards, roadmap planning, stakeholder influence, and mentoring. The role delivers scalable solutions supporting race-day operations and cross-functional analytical products.

Designs and maintains production data ingestion pipelines, layered analytics-ready data models, and reliable automated data foundations. The role integrates client data from APIs, files, Databricks, and shared-data sources; implements testing, monitoring, schema-change handling, and safe reprocessing; and supports application, analytics, and future AI use cases. It also requires collaboration with product teams, strong communication, cloud data warehouse experience, and AI-native development practices.

Design, develop, and maintain data pipelines, ETL processes, integrations, data infrastructure, and complex data models. Build and support data warehouses and analytics solutions using SQL, PL/SQL, Oracle, Snowflake, and cloud technologies. Collaborate with analysts, architects, developers, data scientists, and business stakeholders throughout the delivery lifecycle. Provide production support, troubleshoot issues, tune database performance, and contribute to data-driven reporting and AI tools.

Build and operate cloud data capabilities, including ingestion pipelines, transformations, data models, curated data products, and data-quality controls. Use Snowflake, dbt, SQL, and Python to deliver governed data for analytics, applications, automation, and AI workflows. Implement testing, CI/CD, documentation, lineage, access controls, monitoring, incident resolution, and performance optimization. Collaborate with engineering, analytics, governance, security, and business teams in an Agile, product-oriented environment with active AI-assisted development.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

Lead operational reliability and platform enablement for Databricks: build monitoring, CI/CD, deployment standards, compute and job policies, observability, runbooks, and governance to support secure, cost-aware, production data workloads across regulated environments. Mentor engineers and align platform with cloud/infrastructure and compliance requirements.

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.

Builds and maintains cloud data pipelines, transformations, semantic views, conformed dimensions, and analytical data models. Implements data quality controls, lineage, documentation, testing, release-readiness evidence, and defect resolution. Collaborates with data owners, governance teams, engineers, security stakeholders, data scientists, and customers to deliver reliable, governed data products supporting analytics, reporting, self-service BI, and AI/ML use cases.

Designs and optimizes scalable ELT/ETL pipelines using dbt, SQL, Snowflake, and Azure. Leads data architecture, dimensional modeling, performance tuning, CI/CD, data quality, observability, lineage, governance, and security. Partners with stakeholders to create reliable analytics assets, evaluates GenAI applications, mentors junior engineers, and supports modern cloud data operations.

Support design, development, and maintenance of data pipelines and infrastructure. Assist with ETL/ELT workflows, data ingestion, cleaning, validation, modeling, and warehousing. Document processes and collaborate with data teams to support analytics use cases.

Build and deliver Azure and Microsoft Fabric data solutions for clients. Responsibilities include developing data pipelines with Azure Databricks, Data Factory, Synapse Analytics, and Fabric; writing SQL, Python, and PySpark; supporting data integration, testing, deployments, troubleshooting, CI/CD, and infrastructure management. The role involves consulting with client stakeholders, collaborating with architects and developers, and delivering modern data platforms and reporting solutions.