Top Data & Analytics Jobs

Leads analytics request management and client delivery, develops SQL- and visualization-based solutions, builds media performance reporting, and designs marketing measurement strategies. Collaborates with media, strategy, data science, and engineering teams to connect campaign performance with business KPIs. Mentors analytics professionals, improves data quality and workflows, troubleshoots solutions, and advances measurement practices through attribution, experimentation, predictive insights, and emerging AI-driven approaches.

Own the architecture, reliability, governance, and evolution of the Databricks lakehouse. Design batch and streaming pipelines, develop SQL/Python/PySpark transformations, productionize analytical and AI prototypes, and implement testing, orchestration, monitoring, data-quality controls, governance, and access management. Collaborate with analysts, Engineering, and Product to deliver scalable, cost-effective data solutions for clinical trial insights.

Leads Supplemental and Group analytics and business intelligence initiatives, manages and develops analysts, defines KPIs, oversees dashboards and reporting, and translates complex data into strategic recommendations. Partners with business leaders and enterprise Data and Technology teams on data governance, quality, warehouse initiatives, and transformation. Drives cross-functional operational improvements, communicates insights to senior leadership, and ensures analytics support reliable, actionable decision-making.

Leads the design, optimization, and governance of scalable data architectures and pipelines. Ensures data quality, integrity, security, and compliance; mentors junior engineers; conducts audits and validation; and collaborates on data governance. The role requires expertise in SQL, Python, dimensional modeling, Snowflake on Azure, Azure Data Factory, Databricks, ETL, and data warehousing, with preferred experience in Airflow, Kafka, healthcare, data lineage, and slowly changing dimensions.

Prepares, reviews, validates, and completes client data packages and electronic data deliverables from laboratory testing. Processes deliverables according to client specifications, corrects routine errors, verifies software output, troubleshoots nonconformances, maintains records, meets deadlines, and identifies workflow and data-quality improvements. The role is primarily administrative and office-based, with occasional laboratory exposure.

Designs and manages database architectures, schemas, dictionaries, migrations, performance monitoring, backups, recovery, security, and documentation. Collaborates with developers, analysts, and administrators to maintain data integrity and support applications. Requires extensive database engineering or administration experience, SQL proficiency, knowledge of major database systems, cloud databases, cross-domain solutions, and big data environments. An active TS/SCI clearance with full-scope polygraph is mandatory.

Paid data analyst intern to collect, clean, and analyze e-commerce and marketing datasets, build dashboards, generate customer and performance insights, support market research, and collaborate with marketing, product, and sales teams to drive data-informed decisions.

Leads enterprise people analytics, workforce planning, forecasting, predictive modeling, dashboards, executive reporting, and workforce data governance. Analyzes turnover, retention, hiring, mobility, productivity, talent pipelines, labor markets, and workforce risks. Partners with HR, business leaders, and executives to provide insights, scenario models, organizational design support, and recommendations for strategic workforce decisions.

Designs, builds, and maintains production data pipelines, databases, warehouses, and reporting assets. Owns data from source to consumption, including requirements gathering, data modeling, database administration, performance tuning, and automation. Partners with GTM Operations, Marketing, Sales, Finance, and Product stakeholders to deliver dependable analytics solutions.

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.