Top Senior Data Engineer Jobs in Raleigh, NC

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, enforcing security and compliance, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires extensive infrastructure and data engineering experience across cloud platforms, containers, data security, and workflow orchestration.

Designs, builds, and supports complex ETL and data integration solutions, scalable data pipelines, enterprise data warehouses, and cloud-based data environments. Develops CI/CD pipelines, SQL procedures, batch and real-time processing, and automated testing. Troubleshoots production issues, improves reliability and performance, supports Agile delivery, advises leadership, collaborates with business partners, and mentors junior engineers.

Designs, builds, and maintains Snowflake-based enterprise data warehouse solutions, including data architecture, ETL/ELT pipelines, security, deployment, data quality, modeling, and performance optimization. Partners with integration, analytics, governance, business intelligence, and architecture teams. Supports production operations, resolves ingestion and transformation issues, develops AWS-to-Snowflake scripts, documents implementations, and translates business requirements into scalable data solutions.

Build and own scalable data platforms and ingestion pipelines using BigQuery, dbt, and modern ingestion tools. Design batch and CDC workflows, improve reliability and observability, implement governance and access controls, and support analytics and AI agents. Develop Python services and internal tooling, contribute to architecture and lakehouse experiments, collaborate with stakeholders, and mentor engineers. The role requires strong SQL, Python, cloud infrastructure, testing, CI/CD, and AI-assisted development experience.

Designs, develops, and maintains scalable data pipelines, data warehouses, databases, and analytical systems. Responsibilities include ETL implementation, data modeling, performance monitoring, troubleshooting, infrastructure optimization, process automation, and collaboration with cross-functional teams to support reporting and analytics. Requires 5+ years of data engineering experience and a relevant bachelor’s degree. Financial services experience is preferred.

Leads the design, development, and optimization of scalable data pipelines and analytics platforms. Builds ETL/ELT workflows using Databricks and Spark, integrates and transforms source data into curated models, manages SQL Server data, and maintains documentation and lineage. Collaborates with product, analytics, and engineering teams to deliver reliable data solutions. Applies responsible AI practices and delivers projects independently within established timelines.

Build and maintain secure data pipelines and integrations across business platforms, laboratory systems, analytical instruments, and messy data sources. Own schema design, orchestration, monitoring, quality controls, documentation, and compliant data isolation for export-controlled information. Create a unified lakehouse foundation supporting analytics, dashboards, machine learning, R&D, manufacturing, and operations. Partner with scientists and technical teams to integrate HPLC, GC, Invert Bio, APIs, spreadsheets, PDFs, and semi-structured records.

Administer and optimize Oracle application databases while developing data models, warehouses, ETL pipelines, and analytics solutions across Oracle and Snowflake. Build scalable Python and SQL workflows, support Oracle Analytics Server, manage MongoDB use cases, automate monitoring, and implement CI/CD. Ensure data quality, governance, security, high availability, disaster recovery, and performance across enterprise data platforms.

Own and evolve UJET’s data platform, including MySQL CDC replication to BigQuery, dbt transformations, Terraform infrastructure, pipelines, data quality, observability, and Looker models. Build reliable, analytics-ready datasets and trusted metrics for internal and customer-facing reporting. Partner with Analytics, Finance, Product, and Engineering to deliver scalable data solutions, improve platform reliability, optimize performance and costs, and establish development standards and guardrails.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.

Own and evolve WorkOS’s internal data platform, including ingestion, orchestration, Snowflake, dbt models, data governance, reverse ETL, semantic views, and AI-enabled data workflows. Ensure pipeline reliability, freshness, security, and scalability while partnering with Product, Finance, RevOps, GTM, Engineering, and Security. Build monitoring, runbooks, CI/CD gates, access controls, masking policies, and infrastructure automation across AWS and Kubernetes.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

Design, build, deploy, and maintain AWS-based data and AI/ML infrastructure for enterprise agentic AI platforms. Develop data lake pipelines, observability systems, agent-enablement tooling, and production MLOps/LLMOps capabilities. Own features end-to-end, contribute to architecture and design decisions, optimize reliability, scalability, security, and cost, and collaborate with AWS Professional Services, client teams, and junior engineers.

Build and operate production pipelines for legal records from PACER, court systems, opinions, PDFs, scans, XML, and HTML. Develop LLM-assisted extraction workflows, validate data quality, optimize SQL, and support RAG and semantic search products. Manage AWS S3 and RDS systems within security guardrails while collaborating with engineering teams. The role requires strong Python and SQL skills, legal data experience, document extraction expertise, and autonomous problem-solving with large, messy datasets.

Designs, builds, and supports a near-real-time transactional cache platform using CDC replication from UniVerse to PostgreSQL, transformation pipelines on GCP, and optimized MongoDB Atlas models. Responsibilities include data modeling, indexing, query and performance optimization, production ETL/ELT support, monitoring, incident response, reliability improvements, documentation, and collaboration with API and application teams. The role also mentors junior engineers and contributes to scalable cloud-native data platform engineering practices.

Develop scalable data engineering solutions for Fidelity’s asset management technology platform. Responsibilities include designing data models and ETL pipelines, developing Oracle PL/SQL and SQL solutions, tuning database performance, supporting Snowflake data warehouses, and applying DevOps and Agile practices. The role also provides production support through an after-hours on-call rotation, including troubleshooting and incident response.

Build and own Snout’s data foundation, including PostgreSQL and dbt warehouses, ingestion pipelines, dimensional models, data quality, documentation, and reliable rebuilds. Reconcile data from veterinary practice-management systems, support LLM/AI tooling, translate stakeholder needs into technical plans, and establish the foundation for a future data team and analyst.

Design, develop, and operate large-scale data ingestion, transformation, enrichment, and processing pipelines across cloud, hybrid, and on-premises environments. Build distributed services using Java, Python, and Kafka; optimize data models and platform performance; deploy applications on Kubernetes; support infrastructure automation and CI/CD; troubleshoot production systems; and collaborate with customers and solution architects on implementations.

Designs, develops, integrates, and supports modern data solutions for healthcare claims, revenue, Medicare, and payer data. The role includes on-premises data warehousing, cloud migrations, SQL and Python/PySpark development, dimensional modeling, troubleshooting, performance optimization, and process improvement. The engineer collaborates with engineering, analytics, data science, and product teams in an agile environment.

Designs, develops, and implements modern data solutions for healthcare claims, revenue, Medicare, and payer data. Responsibilities include building and supporting on-premise and cloud data warehouses, migrating workloads to the cloud, dimensional modeling, schema design, troubleshooting, performance improvement, and coding with SQL, Python, and PySpark. The role collaborates with engineering, analytics, data science, and product 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.

Lead the architecture and development of Vercel’s next-generation data platform, supporting batch and real-time integrations, analytics, data warehousing, and AI/ML workloads. Design scalable systems using Kafka, ClickHouse, Tinybird, and Snowflake; establish data governance and security standards; guide architectural decisions and roadmaps; collaborate with engineering, product, security, compliance, and leadership teams; write production code; and mentor engineers.

Architect and lead enterprise data platforms, pipelines, and infrastructure using Spark, Databricks, and cloud technologies. Build scalable data models and dimensional schemas, establish governance and data quality frameworks, optimize performance and reliability, and implement monitoring. Partner with stakeholders to translate requirements into technical solutions, lead design reviews, mentor engineers, contribute to platform strategy, troubleshoot complex failures, and support hiring and team development.

Own Moxie’s end-to-end data infrastructure, including pipelines, Snowflake warehousing, dbt and Omni modeling, orchestration, observability, and documentation. Partner with Engineering and Finance on schema changes, accounting logic, metrics, and business-critical reporting. Deliver financial or historical EMR data models, establish SOPs and reliability KPIs, optimize performance and cost, and help advance Practice Intelligence toward predictive and prescriptive products with appropriate AI safeguards.

Designs, builds, and maintains scalable ETL and data processing pipelines supporting analytics, forecasting, risk, and customer insights. Leads complex SQL development, data modeling, governance, quality, lineage, security, and compliance. Partners with engineering, finance, sales, compliance, product, analysts, and data scientists to translate requirements into reliable workflows. Evaluates emerging technologies, leads proof-of-concepts, and develops automated solutions using Python, Spark, Databricks, cloud platforms, orchestration tools, and modern data warehouses.