Top Senior Data Management Jobs in Los Angeles, CA

Leads a team of five experienced analysts and data scientists while personally owning strategic analytics across ecommerce, health, supply chain, inventory, fulfillment, and merchandising. Partners with executives, establishes KPI and reporting standards, prioritizes the analytics roadmap, and delivers actionable business insights. The role also advances AI-native, self-service analytics capabilities, strengthens data governance and quality, and builds a high-performing analytics culture.

Leads and manages a business analyst team supporting a data platform. Partners with clients, product managers, engineers, and stakeholders to gather requirements, write user stories, perform data gap analyses and source-to-target mapping, validate data using SQL, and support data quality, lineage, QA, Agile delivery, change management, and documentation. Requires healthcare data expertise, people leadership, strong communication, and experience translating technical information for varied audiences.

Leads the design, development, migration, and deployment of scalable data pipelines and cloud data solutions. Establishes reusable ingestion, transformation, quality, governance, compliance, and orchestration processes. Partners with business and BI stakeholders to define data requirements, support financial and operational reporting, and enable AI use cases. Requires expertise in cloud and big data technologies, DevOps, CI/CD, data management, and governance, with preferred experience in Databricks, Snowflake, data warehousing, and finance-domain data.

Leads organization-wide data management, reporting, compliance tracking, data quality, impact measurement, and technology initiative coordination. The role establishes data standards, dashboards, reporting processes, compliance calendars, and corrective actions; coordinates staff and consultants during system evaluation and implementation; translates organizational needs into practical tools and workflows; and trains teams on data quality and reporting practices.

Lead a team delivering actuarial statistical reporting, regulatory submissions, and analytics. Ensure compliance with bureau requirements, drive automated reporting and UAT for policy/claims systems (Guidewire, Solartis), partner with actuarial, technology, and vendors, and manage stakeholder/regulator interactions while mentoring staff and improving data governance and reporting processes.

The Manager, R&D Data Modeler designs and governs conceptual, logical, and physical data models for R&D analytics and digital initiatives. Responsibilities include source-to-target mapping, schema optimization, data integration, metadata and naming standards, FAIR and GxP governance, model versioning, architecture workshops, platform implementation consulting, and mentoring junior modelers. The role supports scalable, interoperable data ecosystems for analytics and AI workloads across data warehouses and cloud data lakes.

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.

Owns go-to-market data governance, quality, enrichment, and automation across Salesforce and connected systems. Designs standards, controls, source-of-truth models, AI and LLM-powered workflows, monitoring, and remediation processes. Partners with Business Operations, Marketing Operations, Customer Success, Finance, IT, and security to improve data accuracy, compliance, reporting, and operational efficiency. Leads data management and automation projects from planning through measurable outcomes.

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.

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.

Owns Kestra’s enterprise data platform, Microsoft Fabric transition, data architecture, governance standards, and Master Data Management program. Leads platform reliability, semantic models, self-service BI enablement, data protection, compliance, vendor resources, and technical teams. Advises executives and functional data owners while preparing governed data assets for AI, automation, and enterprise reporting.

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.

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.

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.

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 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.

Build and lead Inovalon’s third-party data governance program, including data asset inventory, licensing controls, contractual rights management, data intake and release governance, audit readiness, compliance monitoring, and KPI reporting. Partner with Legal, Privacy, Procurement, and business leaders to manage data usage, renewals, access revocation, disposition, and downstream obligations. Develop a governance team while applying expertise in healthcare data, HIPAA, de-identification, and data licensing.

Manage a team of 2–3 analysts while remaining hands-on with SQL, dbt models, dashboards, and complex operational analyses. Lead ambiguous, cross-functional initiatives, deliver recommendations to senior stakeholders, ensure data quality, and improve reporting automation. Partner on AI analytics strategy, data readiness, agent evaluation, and human-in-the-loop governance. Responsibilities include coaching, prioritization, performance management, technical review, KPI development, and operational insights across customer experience, fraud, payments, workforce management, and VIP functions.