Top Hybrid Data & Analytics Jobs

Own and scale automated patient-data ingestion and transformation pipelines from EMR systems into clinical-trial matching workflows. Design reliable asynchronous, queue-based, containerized architectures; manage schemas, validation, data quality, monitoring, and production issues. Partner with AI and product engineering teams on healthcare data foundations, APIs, scalability, and trustworthy clinician-facing workflows. Operate autonomously in a startup environment while applying strong systems thinking and pragmatic technical judgment.

Design, build, and maintain scalable, high-performance data pipelines, ingestion, transformation, and data export solutions. Implement data quality, testing, and monitoring, collaborate with product and stakeholders to build analytical data models, support production jobs, perform PoCs, and contribute to roadmap, standards, and documentation.

Own marketing measurement models for multiple customers, partnering with strategy managers from kickoff through model release, interpretation, enablement, and action planning. Configure and QA model inputs and outputs, manage escalations, deliver deep-dive analyses and scenario work, and support renewals and account growth. Build scalable playbooks, documentation, and AI-assisted workflows while translating complex measurement results into actionable budgeting and campaign decisions. Provide customer feedback to product, science, and engineering teams and support pre-sales and case studies.

Supports analytics and data platform initiatives through simple data transformations, reports, data pipelines, data validation, and documentation. Collaborates with engineers and analysts to improve data quality and enable reliable, scalable data solutions. Requires basic SQL and Python, introductory Azure Data Lake and Databricks knowledge, basic visualization skills, and an understanding of financial data and transaction processing.

Designs enterprise data architecture, models, integration patterns, data pipelines, semantic models, governance standards, and access protocols. The role translates business and application needs into scalable data solutions, supports analytics, automation, AI, and application development, and collaborates with data engineers and stakeholders to deliver trusted, reusable datasets.

The Staff Data Reliability Engineer ensures data integrity, quality, freshness, and availability across enterprise pipelines. Responsibilities include defining data SLOs, implementing observability, building resilient Informatica and Python/PySpark pipelines, automating validation and recovery, eliminating operational toil, leading data incidents and postmortems, creating runbooks, and applying AIOps, machine learning, and DataOps practices to detect anomalies and self-heal data workflows.

Analyze member interactions, safety vetting cases, review decisions, and appeals for quality and policy compliance. Audit Trust and Safety workflows, identify process gaps, track departmental quality, report trends, present data-driven insights, and recommend improvements to processes and training. The role requires strong analytical, communication, confidentiality, attention to detail, and time-management skills.

Develops marketing data strategies, KPI frameworks, governance standards, dashboards, reports, and automated data pipelines. Audits and reconciles multi-source datasets, translates business needs into technical requirements, leads user acceptance testing, and partners with Analytics, IT, and Data Engineering teams. Provides marketing data expertise, documentation, training, and strategic insights on campaign performance, audience behavior, attribution, and personalization.

Manage procurement data and reporting, including spend analytics, data integrity, taxonomy, dashboards, integrations, and ad hoc analysis. Partner with sourcing, FP&A, finance, and leadership to improve reporting, track savings and spend, monitor purchase orders and invoices, and support Travel & Expense strategy. Oversee Ignite and related procurement tools while developing standardized metrics, automated reporting cadences, and Looker visualizations.

Designs, develops, and maintains Workday integrations and enterprise application solutions; translates business requirements into technical designs; troubleshoots production issues; collaborates with stakeholders; ensures security and delivers solutions using Workday integration tools and web services.