Top Data & Analytics Jobs

Analyze clinical data completeness, reliability, and effectiveness across healthcare facilities, vendors, interoperability networks, and integration pathways. Reconcile claims with clinical records, identify missingness and latency, report ADT coverage, evaluate data technologies, recommend vendor configurations, and prioritize facilities for direct acquisition. Partner with product, engineering, and clinical teams to improve scalable, trustworthy data pipelines and support TEFCA and QHIN strategy.

Produces regular, ad hoc, and specialized internal and client reports; manages reporting schedules and distribution lists; investigates data inconsistencies; gathers stakeholder requirements; communicates findings; and compiles data for business analysis. The role requires Excel and Windows proficiency, troubleshooting ability, strong communication and customer service skills, prioritization, and independent work. Previous reporting experience and ProLaw knowledge are preferred.

Leads the architecture of an AI-first enterprise data and analytics platform. Responsibilities include designing data infrastructure, ingestion and transformation frameworks, semantic layers, AI-agent integrations, governance, security, testing, observability, CI/CD, and self-service analytics capabilities. The role sets technical direction across teams, establishes engineering standards, evaluates architectural trade-offs, and mentors analytics engineers.

Provides senior technical leadership for enterprise data engineering platforms, with emphasis on Snowflake, ETL/ELT pipelines, data quality, governance, observability, reliability, and production operations. The role guides architecture and engineering standards, supports incident response and on-call operations, improves platform scalability and resilience, and partners with engineering, infrastructure, security, governance, architecture, and business teams. It is a hands-on leadership position without direct people-management responsibility.

Leads customer data engineering strategy, delivery, team development, and roadmapping. Oversees data-driven product improvements, digital programs, quality initiatives, budgets, cybersecurity remediation, and portfolio management. Manages, mentors, and grows data engineering teams, including contractor teams, while guiding customer data platforms, omnichannel identity resolution, complex data modeling, and personalization-focused data science capabilities. Partners across the organization to execute strategic initiatives and build team capability.

Leads ERP data migration and master data governance for an IFS Cloud implementation. Responsibilities include extracting, profiling, cleansing, mapping, transforming, loading, and reconciling manufacturing, finance, supply chain, and transactional data. Builds Microsoft Fabric pipelines and Power BI models, establishes data quality standards, manages cutover readiness, supports integrations, and coaches business data owners. Requires hands-on ERP implementation experience, advanced SQL, and strong data engineering and governance capabilities.

Conduct management analysis and quality assurance testing for state eServices programs and systems. Develop QA test plans, investigate process improvement opportunities, collect and analyze data, prepare reports and recommendations, provide technical support, troubleshoot issues, and coordinate implementation. The role also supports tax-related compliance research, continuous improvement, metrics, policies, and operational documentation.

Build and scale data insights capabilities, write SQL queries in Snowflake, transform data with Python, and create dashboards in Qlik Sense and Tableau. Support data governance, preprocessing pipelines, predictive modeling, documentation, version control, and automated data workflows. Collaborate with Data Engineering teams to deliver real-time insights for robotics and supply chain automation projects.

Own the company-wide BI strategy, infrastructure, governance, dashboards, LookML models, forecasting, and analytics request process. Partner with executives, business owners, Data Engineering, Product, and Engineering to improve data reliability, translate business questions into recommendations, and surface trends and opportunities. The role requires SQL, BI tooling, and data warehouse expertise, with Python, predictive modeling, and statistical analysis preferred.

Leads the Data Engineering and Analytics team, managing people, technical strategy, intake prioritization, data pipelines, governed reporting, and stakeholder delivery. Oversees Python, SQL, dbt, Airflow, and BigQuery data products while guiding predictive analytics and machine-learning initiatives such as forecasting, recommendations, and segmentation. Establishes quality, reliability, governance, and development standards; mentors team members; supports debugging, process improvement, monthly reporting, invoices, and scorecards. The role also promotes responsible AI adoption and PHI-compliant data practices.