Top Data & Analytics Jobs

The Staff Data Engineer will lead design and implementation of analytics platforms and data products, collaborate cross-functionally, mentor engineers, and drive best practices at Upside.

As a Mid-level Applied AI Data Scientist, you will apply statistical methods and machine learning to derive business insights from data and create Tableau visualizations.

The Senior Analytics Engineer will manage the analytics layer, owning dbt projects, orchestrating data pipelines, defining metrics, and ensuring data quality while mentoring the data team.

Senior analytics leader who defines analytic strategy, leads a team of SQL analysts and BI developers, architects scalable frameworks for medical economics, risk, population health and value-based care, ensures methodological rigor in DBT/Spark and Quicksight dashboards, drives automation and AI integration, presents to executive stakeholders, and mentors senior analytic talent.

Own and build canonical data models and metric definitions using dbt and cloud warehouses. Create tests, documentation, lineage, and data quality checks; reconcile metrics across teams and payer customers; partner with analysts, data scientists, product, engineering, and finance; optimize warehouse performance, cost, and PHI-aware access patterns.

Build the canonical customer operating data layer by integrating Salesforce, Jira, Langfuse, Snowflake, product telemetry and other sources. Surface health and adoption signals, build executive reports and visualizations, implement reconciliation, freshness and backfill logic, support automation and AI summaries, and partner with business and BI stakeholders to translate lifecycle definitions into durable, production-ready workflows and data structures.

Lead and build Coupa's post-sale customer intelligence function: integrate structured and unstructured data, create diagnostic and predictive analytics for churn/expansion/adoption, define AI value measurement, and grow a team to deliver executive-level insights.

Scope, implement, validate, and monitor merchant data integrations to ensure accurate input to data science models. Maintain data configuration, create JSON/CSV templates, write Python/SQL/Spark/Databricks/R scripts for testing and transformation, upload historical data, triage alerts, and coordinate with internal and client teams to resolve data issues and improve pipelines.

Design, build, and maintain scalable batch and real-time data pipelines and data lake architecture. Improve observability and SLOs, optimize ETL/ELT, develop dbt workflows, support event-driven architectures, integrate financial APIs, and collaborate with analytics and ML teams to deliver reliable, model-ready data products.

Design, build, and maintain scalable data pipelines and cloud data infrastructure (warehouse, RBAC, validation). Collaborate with data scientists and engineers, orchestrate ML pipelines, ensure data quality/security, optimize cost and performance, lead incident response and mentor teammates.