Top Data Management Jobs in Los Angeles, CA

Own the enterprise analytics data layer by defining and documenting data models, business rules, metrics, and ETL requirements. Validate curated datasets, maintain gold tables, data catalogs, and governance documentation, and administer the Tableau environment. Support permissions, dashboard adoption, user training, self-service analytics, privacy requests, and data governance while partnering with engineering, business, legal, and privacy stakeholders.

Leads organization-wide data management, reporting, compliance tracking, impact measurement, and data-quality improvement. Builds centralized data standards, dashboards, documentation, and reporting processes; coordinates audits and regulatory requirements; and serves as the internal liaison for a multi-year technology initiative. Partners with departments, consultants, and technology vendors to translate organizational needs into practical systems, workflows, policies, and tools while supporting staff training and continuous improvement.

Coordinates Oracle EPM Cloud data governance, metadata management, integrity reviews, reconciliations, restatements, reporting controls, audit support, and system enhancements. Partners with Finance, Accounting, IT, and business stakeholders to resolve data variances, document requirements, support SOX compliance, maintain reporting outputs, and improve enterprise financial reporting processes.

Leads end-to-end data architecture, engineering pipelines, semantic-layer development, data quality, monitoring, and platform operations. Manages sprint planning, releases, DevOps metrics, project delivery, and stakeholder communication. Establishes MLOps and machine-learning platform capabilities, mentors data engineers, and ensures reliable, well-documented data solutions that support organizational reporting and analytics needs.

Leads the enterprise business intelligence and analytics strategy, manages BI and data analysts, and oversees analytics roadmaps, projects, platforms, dashboards, and vendor relationships. Translates complex data into actionable financial and business insights, supports SAP and Power BI platform upgrades, manages delivery timelines and resources, and coaches team members. The role also drives stakeholder engagement, continuous improvement, talent development, and data-driven decision-making across the organization.

Owns analytical projects across customer onboarding, transaction behavior, and platform operations. Designs dashboards, KPI frameworks, reporting workflows, and data pipelines while delivering actionable insights. Partners with Engineering, Product, Payment Operations, Compliance, and Finance; mentors colleagues; manages project scope and priorities; resolves ambiguity; and drives cross-functional alignment. Requires advanced SQL and data modeling skills, BI tool experience, analytical scripting familiarity, FinTech domain knowledge, and 4+ years of analytics experience.

Own the company-wide data lifecycle across governance, platform coordination, security, and field test operations. Establish data definitions, quality controls, lineage, retention, access management, analytical datasets, and operational procedures. Partner with Foundry engineering to improve ingestion, transformation, monitoring, and scalable architecture. Lead pre- and post-test data readiness, collection, validation, transfer, and labeling processes while building and managing governance and field operations teams. The role is on-site in El Segundo and requires regular travel and support for critical events.

Leads marketing and loyalty analytics strategy, measurement frameworks, forecasting, campaign reporting, experimentation, and lifecycle analysis. Partners with Marketing and Finance to connect performance metrics to business goals, automate reporting, and generate actionable insights. Builds churn, retention, segmentation, and LTV models while evaluating paid acquisition and customer growth opportunities using SQL, Python, BI tools, marketing platforms, and modern AI-enabled workflows.

Leads the design, delivery, and optimization of data architecture, pipelines, semantic layers, data quality frameworks, and Tableau BI platforms. Partners with stakeholders to manage requirements, project plans, sprint execution, monitoring, governance, and release delivery. Guides data engineers, manages team members, and supports AI-enabled analytics capabilities. Requires strong SQL, Spark, PySpark, SSIS, cloud data platform, CI/CD, Tableau infrastructure, and data engineering expertise.

Leads and grows the data engineering team while owning enterprise data architecture, ingestion, modeling, semantic layers, governance, secure APIs, and reliable platform outcomes. Partners with finance, operations, sales, marketing, and product to deliver governed, traceable data for products and AI agents. Sets the roadmap, manages vendors and costs, reviews architecture, contributes code, and delivers cross-functional data platform projects.

Lead Client Legal Services analytics strategy and team development. Own Power BI reporting products, Microsoft Fabric data solutions, governance, predictive modeling, responsible AI adoption, ROI measurement, and executive insights. Partner cross-functionally to improve legal operations, litigation outcomes, staffing, capacity, cost management, quality, and workflow optimization while building scalable analytics capabilities and institutional knowledge.

Manage and grow Ashby’s data analytics team while remaining hands-on with analytical work. The role includes hiring and coaching analysts, improving data practices, prioritizing requests, developing specialization, maintaining analytical quality, and guiding responsible AI adoption. The manager will work across raw and modeled data, dbt models, metric definitions, dashboards, and internal data products, partnering closely with business teams in a remote, high-growth B2B SaaS environment.

Leads advanced analytics for Mattel’s direct-to-consumer ecommerce business, including statistical modeling, experimentation, SQL-based data analysis, dashboard development, measurement strategy, and performance reporting. Partners with engineering, product, marketing, merchandising, and technology teams to translate complex data into business recommendations. The role also improves analytics architecture and governance while mentoring analysts and establishing best practices for scalable reporting and data-driven decision-making.

Build and maintain research data infrastructure connecting laboratory instruments, analysis pipelines, LIMS, repositories, databases, and cloud platforms. Develop ingestion and data transformation pipelines, manage metadata and persistent identifiers, deploy Kubernetes-based services, maintain CI/CD and infrastructure-as-code, and integrate AI/ML tools for data discovery. The role also evaluates data-management technologies, trains research staff, and produces technical documentation and procedures.

Lead and grow data engineering and analytics engineering teams while overseeing data platform modernization, BigQuery and Looker migrations, reusable dbt models, self-service capabilities, governance, reliability, and data foundations for AI and ML. Partner with technical and business stakeholders to prioritize roadmaps, manage hiring and performance, improve adoption, and protect sensitive healthcare data.

Lead and grow a global data engineering team while establishing foundational data platforms and engineering standards. Design and oversee GCP-based pipelines, medallion data models, DataOps, monitoring, SLAs, CI/CD, governance, and data quality practices. Drive AI adoption across engineering workflows and build reliable, AI-ready data. Partner with business and technology stakeholders to prioritize roadmaps, deliver scalable solutions, and support data democratization.

Lead development and execution of data-driven strategies, manage project planning and delivery, mentor and coach team members, ensure quality and ethical standards, design cloud data architectures and governance frameworks, analyze complex datasets to produce actionable insights, and drive continuous improvement using advanced analytics and modern data technologies.

Lead development and implementation of data governance strategies, manage client accounts and deliverables, supervise and mentor teams, analyze data to inform business decisions, ensure compliance with firm standards, and identify improvements in data management processes.

Own the go-to-market master data management roadmap and governance model for accounts, contacts, and organizational hierarchies. Define golden record, matching, merging, survivorship, data quality, stewardship, and SLA standards across Salesforce, billing, finance, marketing, product, support, and warehouse systems. Partner with engineering, systems, finance, legal, and security teams to implement integrations, privacy controls, data products, and remediation programs. Report data health, risks, and business impact to leadership.

Leads Marketing's data architecture, metric governance, analytical modeling, predictive modeling, experimentation, and executive performance reporting. Manages three to four analysts while remaining hands-on with advanced SQL, Snowflake data validation, dimensional modeling, cross-system reconciliation, attribution, customer analytics, and AI-assisted natural-language analytics. Partners with engineering, governance, product, and marketing teams to define data products, ensure analytical accuracy, and establish scalable measurement standards.

Leads the enterprise data platform engineering team and owns its reliability, security, scalability, performance, cost, and operational support. Oversees relational, NoSQL, and analytical platforms; data movement and transformation; infrastructure as code; GitOps; database automation; high availability and disaster recovery; monitoring; compliance; and incident response. Manages engineers, on-call operations, technology selection, vendor evaluations, budgeting, and cross-functional delivery while remaining hands-on in architecture, administration, performance tuning, and complex technical escalations.

Manages HR data, analytics, reporting, dashboards, pipelines, integrations, and automation. Supports compensation, performance management, workforce planning, HRIS maintenance, and employee listening initiatives. Builds data models and AI integrations, audits data integrity, develops actionable insights, and presents findings to HR and executive leadership. Requires strong compensation expertise, advanced Excel, HRIS and BI experience, scripting, data modeling, and handling confidential information.

Leads marketing and loyalty analytics strategy, measurement frameworks, forecasting, experimentation, campaign reporting, retention analysis, churn modeling, and segmentation. Partners with Marketing and Finance to establish performance goals and translate complex data into actionable recommendations. Automates reporting using analytics platforms, marketing automation tools, experimentation methods, and AI-enabled workflows while identifying opportunities to grow and retain high-value consumers.

Leads LTK’s Data Engineering team, owning technical direction, architecture, pipelines, data products, governance, quality, reliability, and platform modernization. Manages and develops engineers, drives roadmaps and complex initiatives, partners with cross-functional stakeholders, and balances scalability, security, performance, cost, and business priorities. Establishes engineering standards, improves operational practices, manages risks and dependencies, and builds trusted, reusable data capabilities supporting analytics, reporting, products, and AI use cases.

Leads a team of 5–8 data engineers and analysts while remaining hands-on with Databricks and Azure data platforms. Owns scalable pipelines, Lakehouse architecture, data products, quality, governance, reliability, security, and cost management. Oversees hiring, coaching, performance, delivery prioritization, agile execution, production health, incident response, stakeholder communication, and cross-team collaboration. Helps shape global data engineering strategy, standards, technology adoption, and platform evolution.