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.

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.

Design, build, and optimize BigQuery-based data solutions and data models. Manage data storage, partitioning, clustering, ETL, and data quality. Collaborate with architects, data scientists, and engineers to deliver scalable, secure cloud data infrastructure and documentation; senior hires provide technical leadership, code review, and process improvement.

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 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.

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.

Lead design, build, and operate scalable, secure GCP data platforms and reusable frameworks (ingestion, transformation, quality, orchestration). Enable self-service consumption and governance-by-design, optimize performance and cost, implement data modeling and observability, enforce security controls, partner with domain teams to onboard use cases, and contribute to platform roadmap and enablement.

Lead design, development, and support of scalable data solutions for counterparty credit risk. Build and maintain data pipelines, reports, and dashboards; collaborate with business stakeholders; resolve technical challenges; lead projects and mentor junior staff; ensure compliance with risk and regulatory requirements.

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

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.

As a Data Engineer, you'll deliver scalable data solutions, participate in technical discussions, and ensure timely, trustworthy data delivery through robust pipelines.

Designs, builds, and maintains scalable ETL pipelines and databases; integrates large-scale data for analytics; develops and tests code, queries, and reports; ensures data quality and governance; leads solution development, trains users, and supports ML and BI initiatives.

As a Data Engineer II, you will design and support ETL/ELT data pipelines, work with AWS, Snowflake, and collaborate within an Agile team on data initiatives.

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.

Build user-facing data products by transforming internal data into actionable metrics. Collaborate with platform teams and data scientists to prototype, validate, and prioritize data sources. Write requirements and proposals, engage in user research, and deliver performant Python/SQL-based data features using Databricks and Postgres.