Top Data Engineer Jobs in Charlotte, NC

Leads enterprise software engineering initiatives supporting financial forecasting, planning, and reporting. Designs, codes, tests, documents, and supports Oracle Essbase 21c solutions and associated infrastructure. Develops ETL solutions using Python, PySpark, and object storage; manages platform capacity, availability, performance, security, middleware, and production issue resolution. Establishes engineering standards, mentors teams, resolves complex technical issues, and supports modernization toward cloud-based data engineering and analytics platforms.

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.

Architect, deploy, and operate core data platform services (Airflow, Superset, Terraform). Build CI/CD pipelines, automate with Python, manage cloud infrastructure, implement security and compliance, develop dbt data models, catalog assets in OpenMetadata, and mentor junior engineers.

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.

Design, build, and own scalable data pipelines and evaluation systems that power production AI features and internal analytics. Ensure data quality across ingestion, modeling, and reporting, collaborate with ML and analytics teams, deploy and monitor ML systems, and establish standards for data work and evaluation.

Leads enterprise architecture and engineering strategy for public-cloud data, analytics, AI/ML, and Agentic AI platforms. Defines cloud-native architectures, engineering standards, governance, reliability, security, and compliance controls. Modernizes legacy data platforms, scales lakehouse and streaming ecosystems, and enables self-service data products. Designs AI-agent frameworks, automation, MLOps, responsible AI controls, and production governance while partnering with engineering, cybersecurity, risk, compliance, and business stakeholders.

Design, build, and maintain optimized, highly available data pipelines and the BMG Data Warehouse. Lead medium-to-complex data integration, data modeling, code reviews, QA, and cloud migration efforts. Collaborate with business partners to translate requirements into solutions, resolve technical issues, and support analytics and reporting needs while adhering to policies and compliance.

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.

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, 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.

Lead design and modernization of scalable, secure data systems and real-time pipelines for xOT cybersecurity. Create data contracts, observability, lineage, and cross-functional solutions supporting cloud and on-prem deployments, Kubernetes, and stream processing to enable threat detection and intelligence.

Develops enterprise data engineering and generative AI solutions, including data pipelines, transformation processes, LLM and RAG applications, AI agents, APIs, and microservices. Builds secure, scalable, governed AI systems with prompt optimization, guardrails, evaluation, observability, and compliance controls. Collaborates with engineering teams and business stakeholders to deliver data requirements, troubleshoot data issues, test solutions, manage risks, and prioritize AI use cases.

Build and maintain production data pipelines, Airflow DAGs, Snowflake infrastructure, and Data Vault 2.0 models. Ensure data quality, observability, security, governance, reliability, and cost efficiency through automated testing and CI/CD. Lead technical projects end-to-end, collaborate with stakeholders, and support SOX-relevant reporting while developing expertise in commercial insurance data and processes.

Design, build, and operate production AI and data systems for marketing: LLM/agent orchestration, retrieval pipelines, data pipelines, CDP and warehouse architecture, automation of manual workflows, data quality and reliability, stakeholder collaboration, and mentoring. Ship production-grade Python and SQL solutions and evaluate/monitor AI systems.

Investigate autonomous-vehicle safety issues through large-scale telemetry and time-series analysis. Integrate vehicle, incident, software-release, location, and operational data into analysis-ready datasets; perform root-cause investigations; distinguish data-quality problems from meaningful outliers; and communicate rigorous findings to safety, operations, and engineering teams. Develop reusable metrics, detection methods, pipelines, and analytics tooling using engineering and physics-based signal-processing techniques.

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.

Designs, builds, and maintains scalable ETL/ELT pipelines and finance data models using Snowflake, AWS, dbt Core, Python, and SQL. Develops Medallion architecture data marts, integrates ERP systems, and partners with stakeholders on financial workflows. Implements data governance, RBAC, automated quality testing, observability, documentation, CI/CD, and secure deployments. Monitors Snowflake costs and optimizes queries while supporting Oracle EBS integrations and the migration to Workday.

Designs and develops complex cloud-native data solutions, scalable batch and real-time pipelines, APIs, data models, and enterprise data platforms. Implements data governance, quality, lineage, security, observability, CI/CD, automated testing, and operational reporting capabilities. Collaborates with architects, product owners, analysts, governance, security, and platform teams on technology strategy and cloud modernization. Mentors engineers, leads technical design discussions, presents recommendations, and supports AI-ready data products and emerging development tools.

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.

Support the design, development, and maintenance of data pipelines, workflows, and cloud-based data platforms. Build reliable data assets through ingestion, transformation, profiling, validation, monitoring, and data modeling. Collaborate with cross-functional teams to support analytics, reporting, automation, machine learning, and AI initiatives. Interns will gain experience with Python, SQL, ETL/ELT, Snowflake, cloud technologies, GitHub, and AI tools while receiving mentorship and professional development.