Top Data & Analytics Jobs

Designs and maintains reliable data pipelines and processing systems for high-volume telecommunications and digital evidence data. Builds data infrastructure supporting AI models and analytics, develops schemas and data models, optimizes performance and scalability, and monitors data quality. Collaborates with AI, data science, and software engineering teams, documents architecture, and follows secure practices for sensitive law enforcement data.

Owns the marketing data and analytics ecosystem, building SQL-backed datasets, Tableau dashboards, KPI reporting, attribution analysis, and experiment measurement. The role monitors data quality, troubleshoots tracking across marketing platforms, analyzes channel and funnel performance, and delivers actionable insights to Marketing, Sales, agencies, and leadership. It also supports budget allocation, lead quality, conversion analysis, and marketing testing.

Own university data integrations from source discovery through validated, signed-off feeds. Build SQL queries and scheduled extracts, model data, support partner upgrades and migrations, translate institutional policies into accurate data feeds, and improve monitoring, documentation, and reusable integration patterns. Collaborate with registrars and database administrators while supporting a named set of university partners. The role requires deep experience with student information systems, including PeopleSoft, and occasional travel for partner onsite engagements.

Leads catastrophe management and modeling, portfolio monitoring, event impact reporting, underwriting and reinsurance analytics, CAT referral processes, and analytical framework enhancements. Partners with underwriting, risk, actuarial, claims, reinsurance, brokers, vendors, regulators, and rating agencies. Oversees catastrophe modeling tools, reporting improvements, reinsurance evaluations, staff training, and strategic risk management recommendations across property business units.

Leads a small team responsible for enterprise data engineering, data warehousing, database administration, integrations, SharePoint, Nintex automation, Power BI enablement, governance, security, and operational reliability. The role owns Microsoft Fabric and EDW architecture, builds and supports ETL/ELT pipelines, manages platform roadmaps and vendors, applies AI-enabled automation, and partners with IT and business stakeholders to deliver trusted, secure, governed data services.

Designs and maintains scalable data pipelines, warehouse models, and analytics solutions. Builds natural-language data interfaces using GenAI and LLM techniques, optimizes SQL, improves data quality and observability, and supports distributed data platforms on AWS or GCP. Partners with engineering, analytics, business, and product teams, contributes to shared codebases, establishes development standards, and participates in production on-call and incident response.

Develop and maintain SQL-based data views, ETL workflows, and cloud data warehouse processes supporting CMS healthcare research data. Ingest new data sources, optimize database performance, document business logic, support production data loads, and collaborate with analysts, technical leads, and CMS partners. The role also involves data quality, monitoring, permissions, recovery, troubleshooting, and occasional off-hours or weekend production support.

Analyze client performance, KPIs, utilization trends, and operational benchmarks using large healthcare datasets. Build reporting views, dashboards, reusable queries, models, and notebooks for Client Success and Operations. Conduct cohort, benchmarking, root-cause, and network opportunity analyses, then translate findings into clear narratives for executives and clients. Partner with analytics leadership to improve analytical standards, documentation, and reusable frameworks.

Build and lead Crown Castle’s Data and AI Governance Engineering capability. Establish governance, data quality, metadata, lineage, access, observability, and AI governance strategies while automating controls and workflows across Databricks and the broader data ecosystem. Remain hands-on with prototyping, testing, troubleshooting, and reusable engineering patterns. Partner with technical, legal, security, risk, audit, architecture, and business teams to improve data trust, accountability, discoverability, and responsible AI adoption.

Build and operate production data pipelines and enterprise data services supporting AI models, agents, and mission applications. Integrate structured and unstructured data across relational, document, vector, and graph systems; establish data quality, lineage, metadata, governance, and secure access practices; and collaborate with AI, platform, government, and commercial engineering teams. Deploy services in Kubernetes and classified environments while developing reusable ingestion, orchestration, documentation, and operational patterns.