Top Data Engineer Jobs in Charlotte, NC

Lead the design, development, testing, documentation, and support of enterprise-scale financial planning, forecasting, and reporting solutions. Manage Oracle Essbase 21c platforms, infrastructure, middleware, capacity planning, availability, performance, security, and production issues. Lead complex initiatives, establish engineering standards, mentor teams, resolve technical challenges, and modernize data engineering capabilities using ETL, Python, PySpark, object storage, cloud platforms, and analytics technologies.

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.

Designs and operates scalable GCP data pipelines, reusable engineering frameworks, feature stores, orchestration workflows, and governed data models supporting AI and machine learning. Builds Python, SQL, and Beam transformation libraries; ensures workload cost, performance, reliability, quality, lineage, and observability; and partners with data scientists migrating models to GCP. The role also documents best practices, provides enablement, and supports production systems during off-hours.

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.

Design, develop, and operate Lowe’s scalable Search Platform using OpenSearch and Elasticsearch. Build cloud-native services, REST APIs, and batch or near real-time data ingestion and ETL pipelines on Google Cloud. Improve indexing, search performance, reliability, and scalability while implementing monitoring, automation, and CI/CD. Troubleshoot production issues, contribute to architecture and code reviews, mentor engineers, and collaborate with platform and product teams.

Leads data engineering initiatives for enterprise counterparty credit risk technology. Designs, codes, tests, debugs, documents, deploys, and supports scalable data solutions and reporting. Collaborates with risk, capital markets, and technology stakeholders; resolves technical challenges, leads projects, mentors staff, and develops dashboards using Python, SQL, Power BI, and related data platforms.

Designs and develops complex data engineering solutions, including data models, ETL frameworks, data ingestion, profiling, quality, governance, metadata, lineage, and lifecycle management. The role analyzes and optimizes data platforms, supports testing and deployment, resolves complex database issues, leads architecture and engineering initiatives, ensures regulatory and security compliance, and mentors team members across technology teams.

Build and optimize HubSpot environments across multiple clients, including CRM architecture, data governance, workflows, automations, integrations, APIs, webhooks, migrations, reporting data, and troubleshooting. Audit data quality, resolve integrity issues, establish scalable standards, document systems, and improve CRM reliability and performance within an agency or client-services environment.

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.

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.

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.

Lead the architecture and delivery of GitLab’s data products, including near-real-time data ingestion, knowledge graph integrations, data marketplaces, observability, and data-sharing APIs. Translate ambiguous 0-to-1 initiatives into roadmaps, resolve cross-team dependencies, ensure operational readiness across deployment surfaces, and mentor engineers. The role requires deep full-stack expertise, technical leadership, and experience with AI-powered orchestration and multi-tenant SaaS systems.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.

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.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

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.

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.

Design and manage financial systems for accurate cashback calculations, build and maintain reconciliation processes, ensure data integrity for yield-bearing instruments, collaborate with compliance, and partner with cross-functional teams to improve financial system efficiency.

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 maintain scalable, AI-native data pipelines and dbt models using Snowflake, SQL, and Python. Optimize performance and costs, develop data quality and monitoring frameworks, support LLM-generated insights, and design enterprise-scale analytical architectures. Responsibilities include dimensional modeling, ETL/ELT automation, debugging, testing, secure coding, compliance, code reviews, and collaboration with data scientists and business stakeholders.

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.

Lead architecture, development, and optimization of a cloud-native data platform. Design high-volume batch and streaming pipelines, optimize data processing across AWS, Snowflake, Redshift, and Athena, and implement Iceberg-based data management. Establish security, privacy, observability, and DataOps practices while mentoring engineers, guiding technical direction, collaborating with data science teams, and delivering scalable petabyte-level data infrastructure.

Design, build, and maintain Keboola-based data pipelines integrating multiple business systems to deliver accurate, scalable data for supply chain, logistics, and transportation analytics; collaborate with analysts and engineers; support documentation, governance, and pipeline automation.