Top Senior Analysis Reporting Jobs in Raleigh, NC

Design and implement data ingestion patterns, scalable data-sharing solutions, and automated Azure data pipelines for migrating a legacy warehouse to a Databricks lakehouse. Use Azure Data Factory, dbt, Medallion Architecture, Java, Python, and AI-assisted development tools while collaborating across teams to ensure secure, scalable, high-performing solutions.

Design and develop data pipelines, integrations, transformations, and infrastructure across AWS, Snowflake, Airflow, and MySQL. Troubleshoot warehouse issues, validate data quality, improve pipeline reliability, and translate business needs into data engineering solutions. Interpret technical findings for client recommendations, manage workstreams, review junior team members’ work, and provide technical guidance across managed services engagements.

Analyze reinsurance data and business processes, gather requirements, create data mappings and flow diagrams, and support FAIRR Data Hub solutions. The role validates data quality, governance, reconciliation, and technical specifications while partnering with business, technology, architecture, engineering, and governance teams. It also contributes to reporting, AI-enabled analytics, solution testing, stakeholder communication, and mentoring junior analysts.

The Senior Data Engineer will transform real-world data into analytics-ready datasets, build scalable pipelines, and conduct analyses to support scientific and business decisions, collaborating closely with data science teams.

Lead the design and implementation of enterprise-scale Knowledge Graphs and Semantic AI solutions, integrating graph intelligence and Generative AI. Manage cross-functional teams in data architecture and governance.

Designs and builds data models, schemas, and ETL/ELT pipelines for analytics, reporting, and machine learning workloads. Optimizes query performance and cloud data infrastructure, establishes data quality and governance practices, integrates datasets with BI and machine learning tools, documents workflows, and mentors junior engineers. Collaborates with data scientists, analysts, and product owners to translate business requirements into scalable technical solutions.

Develop and maintain Medicaid enrollment data and reporting solutions. Write advanced SQL queries, build databases, validate and transform high-volume data, troubleshoot data-quality issues, support audits and testing, and improve controls and processes. Translate Medicaid business requirements into scalable technical solutions and communicate actionable insights, risks, and recommendations to stakeholders.

Build and migrate production data engineering and machine learning workloads to Databricks on Azure. Assess on-premises SQL Server and SSIS systems, develop PySpark and Spark SQL pipelines, implement governance with Unity Catalog, and create reusable CI/CD assets. Work directly with Department of Veterans Affairs teams through requirements sessions, workshops, training, and documentation. Depending on focus, build ML workflows or modernize ETL, reporting, semantic models, and Power BI solutions.

Analyze large datasets in Databricks using Spark SQL and PySpark, create Power BI and Databricks dashboards, build data mappings and Gold-layer data products, and ensure data quality. The analyst will gather requirements, document pipelines and models, apply statistical analysis and machine learning, and collaborate with data engineers and business stakeholders. This is a hybrid Raleigh, North Carolina contract requiring onsite work up to three days weekly through April 2027.

Designs and develops scalable data platforms and architectures using Azure or AWS. Translates business objectives into technical solutions, supports data warehousing, business intelligence, master and reference data management, and develops data-as-a-service capabilities. Leads client initiatives, establishes architectural governance, creates system diagrams, analyzes business processes, advises on emerging technologies, and mentors junior data professionals. The role involves significant client interaction and 30–50% travel, with remote work from a home office when not traveling.

Designs and delivers Finance BI dashboards, data models, reports, and self-service analytics. Translates Finance requirements into technical specifications, investigates data discrepancies across SAP and other enterprise systems, performs SQL-based reconciliation and root-cause analysis, and improves reporting performance and reliability. Leads testing, governance, controls, and stakeholder sign-off while evaluating automation and AI-assisted analytics opportunities.