Top Data & Analytics Jobs

Paid summer data analyst internship supporting the Data Management team. Responsibilities include writing SQL queries, developing dashboards, performing data analysis, compiling reports, and presenting findings to business leaders. The role is mostly remote with two in-office days per week and requires basic Microsoft Office skills. SQL knowledge is preferred, while exposure to Python, Power BI, Tableau, or data engineering is a plus.

Serves as the technical lead for Databricks data modernization initiatives. Designs and optimizes Lakehouse architectures, Medallion data layers, ETL/ELT pipelines, Delta Lake solutions, and cloud data platforms. Supports governance, data quality, analytics, AI/ML, security, DevSecOps, and enterprise integrations. Provides technical documentation, mentorship, stakeholder collaboration, performance optimization, and program support while contributing to Agile ceremonies and technical reviews.

Design and build governed financial data models and marts in Databricks. Partner with Finance, Analytics, and Data Engineering to translate complex requirements into reusable data products. Investigate and standardize business logic, implement reconciliation and data-quality processes, troubleshoot data issues, and improve reliability and scalability. Support Finance modernization through automation, advanced analytics, and AI-enabled solutions.

Own and evolve the enterprise data platform by architecting scalable data models, integrations, warehouses, and pipelines. Build and maintain solutions using Snowflake, dbt, Fivetran, Airflow, AWS, and Power BI. Integrate diverse data sources, implement monitoring and data quality frameworks, troubleshoot production issues, and deliver curated datasets for analytics and AI use cases. Provide technical leadership, establish engineering standards, and collaborate with business, governance, QA, and analytics teams.

Lead development of customer-facing analytics and internal dashboards by building advanced SQL models, optimizing visualizations, and maintaining data models. Drive analytics priorities, improve team processes, present findings to leadership, and participate in agile planning and peer review to support product, operations, and commercial teams.

Performs quantitative catastrophe risk analyses using existing models to estimate client exposure and potential losses from natural and man-made catastrophes. Reviews and validates client exposure data, identifies abnormalities, prepares reports and presentations, communicates with clients on routine projects, and researches scientific and industry information on regional perils.

Designs and maintains AWS data platform foundations, lakehouse architectures, batch and streaming pipelines, tokenization workflows, migration frameworks, governance controls, and DataOps automation. The role uses Spark, Kafka, Iceberg, AWS analytics services, CI/CD, infrastructure as code, and observability tooling to support secure hybrid cloud data and AI initiatives. Responsibilities also include schema governance, cost monitoring, technical documentation, disaster recovery, and cutover planning.

Collect field-based geospatial data using LiDAR and 360-degree mapping systems across California. Configure, calibrate, and operate drone-, vehicle-, or pedestrian-based LiDAR equipment, ensuring accurate data capture. Candidates must provide a qualifying LiDAR system, have surveying or geospatial collection experience, and be able to travel or temporarily relocate within California. Fuel and accommodation expenses are covered.

Lead Jira reporting and analytics for CPE ST3 by building executive, portfolio, and team dashboards; authoring JQL; tracking Agile flow metrics; integrating Jira with Power BI and Confluence; analyzing baseline delivery data; recommending process improvements; and presenting actionable findings to senior Government leadership. The role also supports a live Jira demonstration and operational dashboard deployment requiring active stakeholder access.

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.