Top Data Engineer Jobs in Charlotte, NC

Leads enterprise architecture and strategy for metadata management, data lineage, governance, observability, and AI-enabled data platforms. Defines scalable ingestion, scanning, cataloging, and lineage solutions; expands metadata coverage; supports regulatory and operational requirements; and advances AI-assisted discovery and governance. Provides technical leadership across agile teams, mentors engineers, influences senior leaders, and drives modernization, platform transformation, and cross-domain architectural decisions across a regulated financial services environment.

Designs, develops, and supports analytics data solutions for clients, including Snowflake and dbt ingestion and transformation pipelines, data marts, validation, monitoring, governance, and documentation. Manages multiple consulting projects, budgets, timelines, quality assurance, troubleshooting, client communication, and implementation planning. Requires strong data engineering experience, regulated-industry knowledge, stakeholder communication, and willingness to travel.

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.

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.

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.

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.

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.

Builds and maintains scalable ETL processes, database systems, data products, and real-time or offline analytics tools. Integrates large-scale data, ensures pipeline reliability and data integrity, translates business requirements into maintainable code, and supports analytical solutions such as Tableau dashboards. Partners with stakeholders, troubleshoots production issues, conducts testing and releases, provides data guidance, and trains users on tools and data sources.

Designs, develops, tests, and supports AML data pipelines, reporting solutions, integrations, workflows, and reusable data components. Performs data validation, reconciliation, quality control, troubleshooting, performance optimization, production support, and platform resiliency activities. Translates business requirements into scalable technical solutions, contributes to Agile delivery and testing, documents operational procedures, and collaborates with business, product, and technology stakeholders to improve AML data platforms and meet regulatory needs.

Designs and supports testing of industrial screw pumps for defense applications. Collects, databases, visualizes, and analyzes CMM and pump test data; develops regression and predictive models; automates reporting; documents nonconformances; and creates dashboards for stakeholders. Collaborates with design, manufacturing, quality, sales, and program teams to improve product robustness, testing, engineering standards, and manufacturing processes. Provides customer support and contributes to technical documentation and process improvements.

Leads the design and development of data engineering and AI-enabled solutions for legal, client, and internal business applications. Builds Python and SQL workflows for structured and unstructured data, integrates generative AI and APIs, validates AI outputs, establishes human-in-the-loop quality controls, and supports responsible AI governance. Collaborates with attorneys and technical teams, evaluates emerging technologies, prototypes self-service tools, and provides technical guidance to less experienced professionals.

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.

Build and support scalable data platforms and pipelines, leading migrations such as Snowflake to BigQuery and transitioning reporting to Looker. Responsibilities include data architecture, ETL/ELT, API and marketing integrations, data quality, production troubleshooting, warehousing, performance optimization, and platform modernization. The role partners with analytics and business teams, owns projects through production, documents solutions, and provides technical guidance.

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 scalable, secure data pipelines, models, platforms, and governance systems that enable AI applications to access trusted enterprise data. Partner with analytics, marketing, and product teams to translate requirements into technical solutions, independently deliver projects through production, and support sensitive-data security and access controls. Preferred experience includes LLM architectures, cloud data platforms, infrastructure as code, regulated environments, and technical leadership.

Design and scale lakehouse architecture, including Bronze, Silver, and Gold data layers. Build reliable streaming and batch pipelines with Kafka, Spark, and Airflow; manage Iceberg, Delta, and Hudi table formats; optimize Trino, Starburst, and Databricks queries; and improve platform reliability, data quality, and performance. Collaborate with data scientists, analysts, and product teams to deliver scalable data platform solutions.

Lead development of scalable Java software for endpoint management and IT operations. Design high-availability systems, influence architecture, solve complex scalability challenges, improve quality practices, collaborate cross-functionally, mentor engineers, and own technical initiatives supporting millions of endpoints and servers.

Design and scale lakehouse architecture, streaming and batch pipelines, and reliable data platforms using Kafka, Spark, Airflow, Iceberg, Databricks, and related technologies. Build Medallion-layer data systems, manage open table formats, optimize distributed queries, monitor platform reliability, resolve data quality issues, and collaborate with analysts, data scientists, and product teams.

Build and maintain scalable ETL/ELT pipelines and data foundations using Databricks, dbt, and AWS. Transform raw structured and unstructured data into reliable datasets for product analytics and data science, including telemetry, streaming data, and feature engineering. Develop dashboards in Tableau and Databricks, optimize workflows, and collaborate with Product, Engineering, analytics, and data science stakeholders to support product health, adoption, and performance insights.

Build and ship production AI systems across retrieval, knowledge graphs, inference, agentic workflows, and data platforms. Develop customer-context retrieval, scored signals, evaluation and observability capabilities, and workflows that turn insights into action. Work hands-on with large and small models, durable orchestration, cloud deployment, inference-cost optimization, and high-volume observability data. Establish reusable engineering patterns, collaborate with product and platform teams, and mentor through strong technical contributions.

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.

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.