Top Senior Data Engineer Jobs in Raleigh, NC

The Senior Data Engineer handles ChowNow's data platform, collaborating with teams to enhance data availability and insights, supporting internal and customer-facing products.

Lead the architecture and design of data and analytics solutions, focusing on ETL, data warehousing, and cloud technologies while collaborating with global teams to optimize data processes.

Design and implement large-scale Snowflake-based data platforms, migrate on-prem databases to AWS/Snowflake, build ETL/ELT pipelines and data lakes, create dimensional models, maintain CI/CD and scheduling (Control-M), provide technical leadership and modernization guidance, and develop performant data solutions across multiple database platforms.

Transform Electronic Health Records (EHR) data into structured assets, build scalable data pipelines, and apply Generative AI techniques for analytics within a healthcare-focused team.

As a Staff Data Engineer, lead the technical roadmap for data infrastructure, manage complex data architectures, and mentor engineers while driving the organization's data platform innovations.

Lead design and implementation of scalable, HIPAA-compliant data pipelines and platforms for healthcare ML. Build ETL, orchestration, and tooling for processing EHR, claims, pharmacy, and bioinformatics data; collaborate with data scientists to produce modeling-ready datasets and ensure data quality, reliability, and operational excellence.

The Senior Data Engineer at Unchained will design and implement data pipelines, integrate APIs, ensure data governance, and mentor team members while focusing on accuracy and security.

The Principal Data Engineer will lead the Data Resolution team, focusing on complex data challenges using Spark, system architecture, and mentorship, to optimize graph data pipelines.

The Data Engineer will design and maintain data models on Snowflake, enforce data standards, manage security, optimize SQL, implement CI/CD, and mentor junior staff.

The Lead Data Consultant will design and deliver scalable data solutions, manage project teams, and optimize client data environments, ensuring effective project execution and communication.

Lead design and implementation of a unified event-tracking data platform. Collaborate with Data, Product, and Engineering to build reusable event-publishing frameworks, monitoring/alerting, and deprecate legacy systems. Coach mid-level engineers, influence roadmap and processes, manage cloud/SaaS spend, create documentation, and participate in on-call rotations to ensure platform reliability and data trustworthiness.

Design, build, and optimize distributed query solutions using Starburst/Trino. Enable secure, governed data access across cloud and on-premises sources, implement data federation and query optimization, support IAM and governance in a federal environment, develop backend services and CI/CD, and troubleshoot performance in cloud-native systems.

Design, implement, optimize, and maintain scalable, high-availability database solutions for a SaaS healthcare product. Integrate client data, resolve complex data issues, refactor systems for Azure, optimize MSSQL performance, participate in code reviews, and deliver features within Agile/Scrum to ensure reliable, high-performance data services.

Lead design and delivery of enterprise data centralization and automation for reporting and BI. Build and maintain ETL/SSIS pipelines, data orchestration, file delivery and quality controls, follow SDLC/agile, mentor junior engineers, and partner with business stakeholders to capture requirements and ensure timely data delivery.

The Data Integration Engineer will design, build, and maintain data pipelines and integrations across AWS and Snowflake, ensuring data quality and security while optimizing performance. They will collaborate with various technical teams to implement scalable solutions and document processes for maintainability.

Designs, builds, and maintains cloud-based ETL/ELT data pipelines and platforms on AWS/Azure (Airflow, Snowflake). Develops CI/CD pipelines, performs DevOps, database development, data architecture, testing, and supports multiple project phases to deliver reliable, reusable data solutions.

The Data Engineer at Netflix builds systems for efficient data processing and modeling, collaborating with various teams to empower analytical insights and product features.

The Data Engineer will build scalable privacy-centric data frameworks, engineer data pipelines, and ensure compliance with privacy regulations at Netflix.

Lead and mentor a data engineering team to design and operate scalable ETL/ELT pipelines and Snowflake data warehouse solutions using Python, DBT, Matillion, and AI tools; establish standards, ensure data quality and governance, and collaborate with analytics, product, and BI stakeholders to deliver production-ready data platforms.

Design and maintain high-quality data pipelines, develop DBT models, collaborate with analytics and data science teams, and optimize data operations for scalability.

The Senior Data Engineer will design and build data pipelines and infrastructure to support data science and business intelligence solutions, utilizing AWS services and streaming technologies.

As a Senior Data Engineer, you will design and develop scalable data architectures, manage cloud-native data pipelines, and mentor other engineers while collaborating across teams to support analytics and decision-making.

As a Senior Data Engineer, you will enhance the data lakehouse, optimize data workloads, perform complex transformations, and develop frameworks to improve delivery velocity, focusing on cybersecurity solutions.

Design, build, and maintain production data pipelines to/from BigQuery; implement data quality and monitoring; maintain ingestion frameworks and dbt source/staging layers; troubleshoot pipeline issues; contribute to ML pipeline productionization and evaluate event-driven and analytic storage formats.

Lead design and implementation of scalable multimodal data pipelines, active learning and labeling loops, training and inference engineering, and model serving/optimization. Focus on data cleaning, OCR/layout normalization, sample construction, observability, and cost-efficient multi-model routing.