Top Analysis Reporting Jobs in San Diego, CA

The Analytics Manager will lead analytics for the Payments and Identity lifecycle, manage a team of analysts, develop KPIs, and ensure data quality while collaborating with cross-functional teams.

The Data Administrator maintains data integrity and compliance, oversees health plan submissions, conducts eligibility checks, and provides reporting and insights for organizational operations.

Conduct research and development to enhance NLP models aimed at improving market research methodologies using AI and Crowd Intelligence.

Provide production support and enhancements for SAP BW/BOBJ/HANA and EUNA Budget environments. Develop and optimize BW data models, queries, reports, and integrations (including Snowflake and Power BI). Troubleshoot production issues, perform impact analysis, and provide architectural guidance for reporting, analytics, and budget planning solutions.

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).

Design and build Microsoft Fabric and Synapse analytics solutions (lakehouses, warehouses, pipelines, notebooks, semantic models). Develop Power BI reports and semantic models with DAX, performance tuning, and RLS. Integrate Dynamics 365/Dataverse data, author SQL/Python/PySpark transformations, manage source control and deployments, run client workshops, and build AI-enabled experiences.

Own and define operational metrics in BigQuery; validate and monitor data quality; build dashboards, lineage, and tooling; partner with engineering, product, and operations to turn analyses into repeatable KPIs and process improvements.

Design, build, and scale cloud lakehouse data products (Databricks/Snowflake) from ingestion to analytics-ready datasets using medallion architecture. Develop reusable Python frameworks, Spark pipelines, and dbt/SQL transformations. Implement data modeling, governance, observability, validation, and SLA monitoring. Troubleshoot production issues, adopt AI to accelerate engineering, and provide technical leadership, code reviews, architecture decisions, and mentoring for global engineering teams.

This role requires entry-level talent in SQL and database technologies, providing training in MS SQL, BI tools, and data warehousing methodologies. Candidates will learn and eventually be placed in related positions after training.

The role involves architecting data solutions in Azure's Big Data environment, focusing on integration and understanding of Big Data technologies.