Top Data Engineer Jobs in Charlotte, NC

Lead enterprise-scale Akeneo PIM implementations, including solution architecture, platform configuration, data architecture, API development, and third-party integrations. Collaborate with developers, architects, clients, and stakeholders to deliver digital transformation projects. Ensure data privacy, security, and lifecycle protection while documenting technical solutions, gathering requirements, managing expectations, and mentoring junior team members.

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.

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 data pipelines and dimensional models using Spark, SparkSQL, and cloud lakehouse technologies. Own pipelines from requirements through deployment, monitoring, and iteration; improve data quality, lineage, reliability, and cost efficiency. Partner with data scientists, analysts, product managers, and engineers to support datamarts, KPIs, reporting, and analysis. Participate in business-hours on-call rotations and improve runbooks and alerting.

Build scalable, governed analytical data products, semantic layers, reusable data models, certified datasets, and AI-ready analytics foundations. Perform validation, quality checks, optimization, documentation, and lineage management. Collaborate with Analytics, Product, Engineering, Data Engineering, and business stakeholders on project planning and metric governance. Improve self-service reporting, dashboard performance, automation, and data consistency while mentoring junior team members.

Leads architecture, development, modernization, and integration of enterprise finance technology platforms. Designs scalable data pipelines using Python, Spark, and Kafka; supports financial transaction processing, accounting engines, regulatory reporting, and enterprise data warehouses. Provides technical leadership, establishes engineering standards, drives cloud, DevOps, CI/CD, security, and resiliency practices, resolves complex production issues, and mentors engineering teams while partnering with finance, accounting, risk, audit, and technology stakeholders.

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.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

Architects, deploys, and operates CrowdStrike’s data platform infrastructure and services. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and access controls, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires cloud, container orchestration, SQL, data modeling, compliance, and AI technology expertise, along with eligibility for CJIS clearance.

Leads the design, implementation, automation, integration, and support of enterprise DLP and data protection solutions. Maintains sensitive-data feeds and integrations, troubleshoots complex security issues, performs risk assessments, evaluates technologies, and provides technical guidance. The role also mentors engineers, drives automation and operational improvements, and responsibly applies AI-assisted development tools while ensuring security, compliance, privacy, and ethical standards.

Build and maintain Snowflake data models using dbt and orchestration tools, improve data quality and observability, define data contracts, and support a modular healthcare data platform. Collaborate with analysts, data scientists, and software engineers to deliver trusted datasets. Monitor and troubleshoot pipelines, optimize Snowflake performance and costs, participate in design reviews, document systems, and support governance, security, and privacy standards for healthcare data.

Build, test, document, and optimize Snowflake data models using dbt. Improve data quality, observability, lineage, and data contracts while collaborating with analysts, data scientists, and software engineers. Monitor pipelines, troubleshoot incidents, optimize query and storage performance, support client-specific configurations, participate in design reviews, and contribute to scalable data platform patterns. The role also requires clear documentation and awareness of security, governance, privacy, and healthcare data requirements.

Build and operate low-latency market data systems for institutional trading, including feed handlers, normalization pipelines, venue connectivity, and distribution services. Develop high-throughput services, improve reliability and performance through observability and incident response, participate in on-call support, and collaborate with engineering and product teams. The role requires production backend engineering experience, market data infrastructure expertise, Java or C++, messaging frameworks, and exchange connectivity protocols.

Leads enterprise data platform architecture and modernization, including migration from Azure SQL Server to Databricks, Lakehouse design, Kafka streaming, Spark optimization, governance, reliability, and CI/CD. Enables AI and agentic data capabilities through governed Delta tables, feature stores, vector stores, RAG pipelines, and Databricks tools. Defines engineering standards, security, disaster recovery, and observability practices while mentoring engineers and partnering cross-functionally on scalable data solutions.

Define and govern enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, transactional, and AI use cases. Establish standards for data quality, lineage, access, cataloging, governance, observability, and SLAs. Architect AI-enabled workflows, resolve complex architecture issues, influence roadmaps, and mentor engineers through hands-on technical leadership. The role requires 15+ years of software, data engineering, or architecture experience and expertise in large-scale data platforms and modeling.

Designs, builds, and maintains enterprise data structures, pipelines, storage systems, frameworks, and microservices. Develops scalable solutions using big data technologies, prepares data assets for analytics and business use cases, and ensures data quality, integrity, security, and regulatory compliance. Partners with data scientists, modelers, technology teams, business stakeholders, and executives to translate needs into data solutions and support strategic decision-making.

Designs and maintains secure, scalable data pipelines for structured and unstructured sources. Builds segmented data architectures with access controls, classification, governance, lineage, and auditability for sensitive environments. Optimizes workflows across cloud and hybrid platforms, supports production systems, and partners with technical, business, compliance, and cybersecurity stakeholders. The role enables analytics, AI/ML, and digital products while ensuring data integrity, performance, observability, and resiliency.

Partner with sales teams to win strategic data security opportunities. Present technical solutions, gather customer requirements, lead product evaluations, configure solutions, and design evaluation test plans. The role requires extensive pre-sales experience, security and networking expertise, data protection and web technology knowledge, and collaboration across matrixed go-to-market and partner teams.

Designs and maintains scalable data pipelines, warehouses, lakes, integration solutions, and analytics platforms. Develops AWS and Snowflake data solutions using Python, SQL, dbt, ETL/ELT, and Data Vault modeling. Supports replication, APIs, governance, metadata, lineage, and data quality across enterprise environments. Troubleshoots complex data issues and provides technical leadership while collaborating with engineering, architecture, analytics, and business teams.

Design, develop, and operate large-scale data ingestion, transformation, enrichment, and processing pipelines across cloud, hybrid, and on-premises environments. Build distributed services using Java, Python, and Kafka; optimize data models and platform performance; deploy applications on Kubernetes; support infrastructure automation and CI/CD; troubleshoot production systems; and collaborate with customers and solution architects on implementations.

Designs, builds, and operates scalable batch and streaming data pipelines on Databricks for federal missions. Responsibilities include developing Spark and Delta Lake solutions, managing clusters and workflows, implementing Unity Catalog governance and security, optimizing ETL/ELT processes, integrating CI/CD, supporting machine learning and advanced analytics, monitoring data quality, and collaborating with technical teams and stakeholders.

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.

Designs and maintains data warehouse infrastructure, semantic and dimensional models, metrics layers, dashboards, reporting, and secure data pipelines. Builds governed self-service analytics in Sigma, Power BI, and Databricks; defines KPIs and data standards; implements row-level security and Entra ID controls; supports data quality, observability, AI/ML use cases, and BI adoption. Collaborates with technical and business stakeholders in an Agile-Scrum environment.

Build and maintain production data ingestion pipelines, transformation models, warehouses, and schemas for PerfectServe’s data platform. Deploy reliable, tested pipelines using idempotency and data-quality checks, apply dimensional and medallion modeling, participate in design reviews, and document owned pipelines and runbooks. The role requires SQL, programming experience, dbt or similar transformation tools, cloud data warehouse experience, ELT orchestration, AWS knowledge, and a strong data-testing mindset.

Designs, builds, and maintains enterprise data pipelines, ETL processes, integrations, data infrastructure, and complex data models. The role supports analytics partners, develops scalable data warehouse solutions, tunes SQL and databases, troubleshoots production issues, and collaborates with architects, analysts, developers, and stakeholders. Responsibilities include end-to-end project delivery, data modeling, big data engineering, production support, agile planning, and mentoring team members.