Top Hybrid Data & Analytics Jobs

The Analytics Engineer will design and maintain data infrastructure for business intelligence, optimize analytics processes, and ensure data quality and governance.

The Senior Data Scientist will optimize marketing investments through statistical and machine learning models, working closely with finance and engineering teams.

The Staff Data Scientist will work with product teams to analyze data, identify improvement opportunities, design experiments, and create reporting dashboards to enhance contract management workflows.

The Research Engineer will research, test, and deploy trading strategies while designing research infrastructure and collaborating on innovative market ideas.

The Sr Manager, Operations Analytics & Reporting leads analytics and reporting functions, develops insights into operational performance, and directs forecasting and planning initiatives to improve decision-making processes across consumer lending and customer service operations.

Design, build, and maintain Snowflake data models, warehouses, and ELT/ETL pipelines; develop backend services in Python and C#; manage CI/CD and deployments; troubleshoot production issues; collaborate with clients and mentor junior engineers.

Own and improve the company data stack: expand the data warehouse, build and streamline Python-based ingestion and ETL processes, refactor dbt models, maintain data quality, standardize reporting, and collaborate with engineering and analysts. Act as technical owner for parts of the stack and proactively drive improvements and automation.

Support VA IT Budget and Finance by sustaining and enhancing the TBM Cost Transparency Model using Apptio. Analyze, validate, and map financial and operational data to TBM taxonomy, build data workflows and ETL processes, support data governance, automate financial reporting, and assist senior staff in client meetings to enable data-driven IT budgeting and investment decisions.

Design, build, and iterate data engineering pipelines, dashboards, and reports to support analytics and data science. Partner with stakeholders to translate requirements into engineering solutions, promote self‑service analytics, define best practices, and support implementation using Databricks, Snowflake, and Azure components.

Lead the analytics data layer: design and operate a ClickHouse-backed warehouse, define and validate trusted metrics, build customer-facing React dashboards and internal Grafana/self-serve tooling, and ensure data quality, lineage, and documentation for telemetry-derived insights.