Top Data Engineer Jobs in Charlotte, NC

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.

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.

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.

Develop and maintain cloud-based data pipelines, integrations, and ETL/ELT processes supporting analytics and operational reporting. Build Snowflake and Azure solutions using Databricks and Storage services, assist with cloud migrations, and support API and MuleSoft integrations. Troubleshoot pipeline issues, ensure data quality and governance, participate in testing and code reviews, and collaborate with technical and business stakeholders. The role also requires documentation, secure data practices, and continuous learning.

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, 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.

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.

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.

Build and maintain production-grade batch and streaming data pipelines in Databricks using PySpark and SQL. Develop ingestion frameworks, data models, APIs, data quality checks, monitoring, and Medallion Architecture across bronze, silver, and gold layers. Integrate diverse data sources, optimize Spark workloads, document data lineage and architecture, and collaborate with engineering and federal stakeholders. An active Secret clearance or higher is required.

Designs, develops, and maintains scalable cloud-based data solutions, ELT/ETL pipelines, data warehouses, curated datasets, and transformation logic using Snowflake and related tools. Optimizes SQL and warehouse performance, monitors workflows, supports data governance and quality initiatives, documents data flows, and delivers trusted datasets for Tableau reporting. Collaborates with business and technical stakeholders in Agile processes while ensuring secure handling of sensitive PHI data.

Leads the design and implementation of scalable data architectures, ETL pipelines, data warehouses, migrations, and cloud data solutions. Provides technical leadership through system analysis, requirements gathering, design documentation, code and architecture reviews, SDLC management, production deployments, and mentoring junior engineers. Develops integrations using multiple programming languages, ETL platforms, cloud services, APIs, and CI/CD tools.

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.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

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.

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, 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.

Builds and maintains reliable, scalable data pipelines for enterprise analytics. Responsibilities include data integration, transformation, profiling, quality monitoring, metadata management, SQL development, data mapping, ETL/ELT, testing, documentation, and performance optimization. Collaborates with analytics, warehousing, database, technology, and business teams in agile environments to deliver fault-tolerant solutions across legacy, cloud, web, and packaged data sources.