Top Hybrid Data & Analytics Jobs in Los Angeles, CA

Analyze and improve business systems, workflows, data quality, reporting, and operational processes. Administer and optimize Deltek Vantagepoint, develop Power BI dashboards and data models, validate reporting accuracy, support CRM and financial data, and translate business requirements into scalable system solutions. Provide user support, documentation, training, testing, and implementation leadership while collaborating across Finance, HR, Marketing, Operations, and project teams.

Develop marketing analytics, KPIs, reporting methodologies, dashboards, visualizations, and actionable insights for client campaigns. Query and analyze data, identify optimization opportunities, communicate findings to technical and nontechnical audiences, collaborate with media planning and brand strategy teams, and manage client expectations. The role also improves analytical workflows, supports measurement strategies, and presents key findings throughout client engagements.

Develops and maintains metadata workflows for Getty’s Linked Open Data environment using Arches. Responsibilities include metadata creation and curation, semantic data mapping, crosswalks, cleaning, transformation, analysis, reporting, scripting, testing, documentation, and data integration. The role applies linked-data standards and computational methods, supports interoperability and reuse, collaborates across teams, and contributes to outreach, presentations, and publications related to cultural heritage data.

Leads qualitative and quantitative consumer research initiatives from scoping through delivery. Manages brand health trackers, analyzes sales and panel data, develops secondary research, and produces concise insights to guide strategic decisions. Partners with vendors and senior stakeholders, presents findings, manages multiple projects, and serves as a strategic thought partner across the organization.

Develop and administer enterprise business intelligence solutions using Power BI, SSRS, SQL Server, and data warehousing technologies. Design data models, dashboards, visualizations, KPI scorecards, security models, and analytical solutions for large-scale datasets. Optimize queries, ensure data quality and governance, establish BI standards, and provide guidance to other developers. Collaborate with engineers, database administrators, and business partners to translate complex data into actionable insights.

Design, build, and operate Azure-based data pipelines and integrations. Consolidate data from multiple sources into centralized SQL systems, perform transformations and field mapping, and maintain data warehouse capabilities. Troubleshoot quality, integrity, latency, security, and reliability issues while applying governance, monitoring, and SLA practices. Collaborate with engineers, analysts, and stakeholders in Agile pods to deliver reusable, analytics-ready data assets.

Design, build, and operate complex Apache Airflow workflows, Snowflake and dbt data transformation pipelines, and distributed data science inference systems across multi-cloud environments including AWS and GCP. Collaborate with an experienced data engineering team while applying systems engineering and product lifecycle principles.

Develop and maintain scalable dbt models, dimensional data models, semantic layers, metrics, documentation, tests, and data governance on Snowflake and modern data platforms. Partner with cross-functional stakeholders to define KPIs and deliver trusted self-service analytics. Investigate data quality issues, optimize workloads, improve platform reliability, mentor junior team members, and explore AI-driven workflow automation.

Own and evolve the end-to-end product analytics platform, including instrumentation, event collection, ingestion, raw data storage, data pipelines, analytical models, metrics, dashboards, quality monitoring, observability, lineage, and governance. Partner with software engineers to ensure analytics data is accurate, scalable, traceable, and reliable for business and product decision-making.

Analyze survey and secondary data using statistical and multivariate techniques. Clean, structure, code, and summarize datasets; use SPSS, R, Excel, Python, and VBA to conduct analyses and automate procedures. Prepare Excel outputs and PowerPoint presentations, explain findings to internal and external clients, support analytic planning, and ensure accurate, timely delivery of market research work.