Top Hybrid Data & Analytics Jobs

Designs, builds, and maintains scalable batch and streaming data pipelines, data warehouses/lakes, and ETL/ELT processes. Collaborates with BI, Data Science, and Architecture teams to model data, validate sources, ensure data quality, optimize performance, and enable analytics using cloud and big-data technologies (AWS, Snowflake, Databricks, Spark, Kafka).

Lead development and operations of geospatial data pipelines and lakehouse systems. Migrate orchestration to Prefect, extend sources and transforms, optimize Postgres and Iceberg stores, build observability and AI-agent readiness, and integrate cross-team tooling with product and ML teams.

Lead quantitative analyses of loan originations and portfolio performance to identify credit risks and improvement opportunities. Build multivariate/predictive models, conduct diagnostic and funnel analyses, measure costs/benefits, design monitoring and controls, present recommendations to VP-level stakeholders, and drive hands-on implementation using SQL, Python, Excel, AI tools, and financial modeling.

As the Founding Data Lead, you will establish the analytics function, manage high-volume data, and provide insights to inform business decisions.

The Staff Data Scientist will drive data-informed monetization strategies across subscriptions, advertising, and virtual currencies, collaborating with product and engineering teams and defining success metrics.

The Sr. Product Analyst will manage data analysis processes, providing insights into user behavior and business performance to drive growth decisions, while mentoring junior analysts.

The Customer Analyst will own analytics initiatives, maximize sales through analytics, plan production, and collaborate cross-functionally to improve customer outcomes.

As a Lead Data Engineer, you will architect data foundations for AI applications, lead a team, optimize data pipelines, and ensure data governance.

Perform data analysis and actuarial projections using Excel, SQL, SAS, Python and Milliman tools for health insurance consulting. Develop and run actuarial models, analyze data, review peer work, assist research and product development, and support client deliverables within project teams.

Lead design, build, and maintenance of scalable data pipelines and lakehouse infrastructure using PySpark, Databricks, and Airflow. Own data & ML platform (Terraform, Databricks), improve observability, data quality, and developer tooling, and partner closely with Data Science and engineering teams.