Top Data Engineer Jobs in Charlotte, NC

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, apply data modeling and DAGs, use Databricks, Airflow, and Hadoop, and collaborate with clients to deliver actionable insights.

Lead design and implementation of data infrastructure, pipelines, and integrations using cloud platforms. Manage teams and client accounts, ensure data quality, security, and compliance, deploy scalable solutions (Databricks, Snowflake), mentor junior staff, and identify data-driven business opportunities.

Senior Data Engineer on PwC's Managed Data, Analytics & Insights team to design, build and manage advanced data ecosystems. Responsibilities include designing data solutions and scalable pipelines, solving complex problems, mentoring junior staff, maintaining high delivery standards, and building client relationships while aligning solutions to business context.

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.

As a Senior Associate in Data Engineering at PwC, you will design and implement data architecture strategies, analyze complex issues, mentor junior members, and build client relationships while ensuring adherence to project standards.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

As a Manager in Managed Services, you'll lead data ecosystems projects, mentor juniors, manage client accounts, and deliver data strategies to optimize operations and drive insights for clients.

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

As a Data Engineer, you'll lead data engineering projects, develop data transformations, build workflows with Apache Airflow, and ensure data quality and integrity while collaborating with stakeholders.

Design, build and maintain scalable data pipelines and automated workflows using Python, Airflow, DBT and cloud warehouses. Partner with Analytics, Sales and Marketing to deliver curated marketing datasets, enforce data quality through validation and CI/CD, and support production deployment and monitoring of Enterprise Data Lake/ODS solutions.

The Data Engineer will build and maintain data pipelines, optimize workflows, manage infrastructure, and enhance pipeline observability while collaborating with teams for effective data delivery.

The Data Engineer will help build modern data infrastructure, designing ETL pipelines, modeling datasets, and collaborating with teams for improved data quality and accessibility.

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.

The AI Automation Lead will architect and deliver multi-agent AI workflows, develop full-stack applications, and lead technical decisions across engineering teams.

As a Senior AI/Data Engineer, you will design and maintain scalable data and AI infrastructures, partnering with cross-functional teams to deliver reliable data systems and support AI-driven business capabilities.

Build and maintain reliable data pipelines, Airflow DAGs, and Snowflake-based Data Vault/warehouse models. Implement CI/CD, automated testing, observability, and production support while partnering with stakeholders and developing insurance domain expertise.

The Senior Data Engineer will design, build, and operate scalable data pipelines for high-performance racing programs, utilizing cloud architecture and streaming systems to support real-time data delivery.

Build, test, and maintain dbt/SQL transformations and pipelines in Snowflake; assist with dimensional modeling, data quality, documentation, and governance; collaborate with analysts and business users; use Git workflows; apply analytical problem-solving; learn observability and AI-assisted engineering under mentorship.

Lead design, build, and operate scalable, secure GCP data platforms and reusable data frameworks. Implement ingestion/transformation (batch/streaming), orchestration, data modeling, governance, observability, and CI/CD to enable self-service analytics and enterprise AI.

Lead design, build, and operate reusable, secure, cloud-native data platforms on Google Cloud. Develop ingestion/transformation frameworks (batch/stream), orchestration (Composer/Workflows), shared libraries (Python/SQL/Beam), data modeling, governance-by-design, observability, cost/performance optimization, and enable self-service data consumption across teams.

Design, build, test, and maintain data management processes and analytical applications for medical research. Translate requirements into data architectures, develop ETL, queries and reports, coordinate testing and deployment, tune performance, and communicate results to clients. Serve as subject-matter expert for internal data warehouses.

Lead design and implementation of AI-ready data platforms and scalable batch/streaming pipelines to support Generative AI and RAG solutions. Own architecture, reliability, governance, observability, and operationalization of production GenAI data systems across cloud environments. Mentor engineers and collaborate cross-functionally to integrate AI data products into enterprise workflows.

Lead development of AI and ML solutions for healthcare systems, overseeing teams, driving use case development, and ensuring operational excellence while managing global delivery operations.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.