Top Senior Analytics Jobs in San Antonio, TX

Analyze and optimize drug pricing using data-driven models, dashboards, reports, and strategic frameworks. Partner with Sales, Operations, Marketing, Finance, Product, pharmacy partners, and data teams to identify opportunities, define success metrics, improve reporting, and balance pharmacy economics with patient affordability. Serve as a trusted analytical advisor and support backend data development and query optimization.

Leads a client-facing healthcare analytics team that designs and maintains prior authorization decision automation programs. Consults PaaS clients, develops strategic roadmaps, translates requirements into technical blueprints, analyzes authorization and claims data, and builds KPI reports using visualization tools. Manages direct reports, improves analytics processes and AI tooling, partners with product and engineering teams, and ensures automation outcomes and client KPIs are achieved.

Designs and maintains scalable data infrastructure, ETL/ELT pipelines, canonical datasets, and AI context layers. The role improves data quality and alignment across sales and finance systems, investigates data inconsistencies, leads requirements through UAT and implementation, supports analytics self-service, and partners with cross-functional teams to deliver process improvements and reliable business data.

Own marketing measurement from initial engagement through closed-won revenue. Manage cross-channel reporting tied to Salesforce pipeline, tracking across GA4, advertising platforms, SEO, and AI search. Validate multi-touch attribution, analyze marketing experiments, model budget impact, and recommend investment priorities. Present performance insights to marketing and sales leadership across global regions.

Build Deepgram’s governed decision layer above warehouse gold tables. Responsibilities include modeling cross-system entities, owning semantic and metrics definitions, creating agent-facing data tools and MCP surfaces, implementing data quality assertions and reconciliation checks, evaluating AI agent accuracy, and answering business questions while leaving reusable models behind. The role requires production-grade SQL and Python, semantic-layer ownership, strong data modeling judgment, AI-native workflows, clear writing, and comfort building infrastructure from scratch.

Leads enterprise data and analytics strategy, architecture, modernization, and delivery across cloud and on-premises environments. Oversees data platforms, engineering, integration, governance, quality, lineage, security, and compliance. Partners with executives to define roadmaps and investments, manages cross-functional teams and vendors, enables AI/ML capabilities, and mentors data professionals while driving scalable, trusted data solutions and measurable business outcomes.

Manages programmatic analytics and insights for client accounts, leading measurement frameworks, brand lift studies, incrementality testing, and A/B experiments. Develops media and campaign strategies, communicates performance insights to clients, and supports pitches and QBRs. Oversees, mentors, and develops analysts while partnering with Product, Engineering, Sales, and Customer Success teams to improve measurement tools, campaign optimization, and analytical capabilities.

Leads business and data analytics strategy across information management, governance, reporting, analytics delivery, and data platforms. Oversees strategic initiatives, data modernization, reporting optimization, risk controls, and cross-functional teams. Provides executive-level insights, guides architecture and technology decisions, manages priorities and capacity, and drives data-informed business outcomes. Requires substantial analytics experience in financial services, people leadership, large-scale data analysis, Agile project leadership, and strong executive communication.

Owns fraud performance analytics for depository products, including monitoring losses, approval rates, false positives, customer friction, and risk metrics. Builds data models, transformation pipelines, dashboards, scorecards, and monitoring frameworks. Conducts fraud deep dives and develops optimized rules, policies, thresholds, models, and decision strategies. Partners with Product, Engineering, Machine Learning, Data Engineering, Fraud Operations, and Risk teams to improve fraud detection and support product launches.

Leads analytics strategy and technical delivery for clients, specializing in Adobe Analytics and Customer Journey Analytics. Conducts measurement workshops, defines KPI and implementation strategies, oversees tagging and data quality, guides experimentation, and reviews dashboards and insights. Advises senior stakeholders, manages and mentors consultants, supports proposals and account growth, and contributes to Adobe Experience Platform initiatives involving Target, RT-CDP, and AJO.

Designs and optimizes Power BI dashboards, semantic models, datasets, DAX measures, and reporting solutions for identity governance and administration analytics. Partners with ETL engineers and stakeholders to validate Parquet data from Azure Synapse and SailPoint sources, establish data-quality checks, monitor refreshes, define KPIs, and document data lineage. Supports compliance reporting, access governance, audit readiness, certification metrics, and Zero Trust analytics.

Build and operate Imagine Worldwide’s AWS- and ClickHouse-based analytics platform, including infrastructure, data lakes, Spark processing, ingestion pipelines, Airflow orchestration, dbt models, CI/CD, monitoring, security, backups, and cost optimization. Process billions of device events and integrate program context into reliable analytical datasets. The role also involves incident response, documentation, architectural contributions, and collaboration with globally distributed analytics and operations teams.

Analyze marketing and customer data to measure campaign impact, uncover customer insights, and improve marketing ROI. Build Tableau dashboards, write and optimize SQL queries, conduct segmentation and lifecycle analysis, validate data quality, and provide actionable recommendations. Partner with marketing, product, and analytics stakeholders to translate business needs into analytical solutions and support strategy across channels, including spend allocation, targeting, attribution, and campaign effectiveness.

Build and lead Surescripts’ commercial analytics function, including governed Salesforce and CRM data models, KPI definitions, dashboards, forecasting, pipeline analytics, customer journey insights, automated reporting, quota modeling, and territory planning. Manage an analyst and partner with Sales, Marketing, Finance, Data, and Business Applications. Present actionable recommendations to senior leaders while improving data quality and enabling self-service analytics.

Own end-to-end production data platforms, writing Python and SQL, designing AWS pipelines, modeling analytics data, and implementing quality and reconciliation controls. Make architecture decisions independently, ensure correctness and auditability, and use AI coding tools effectively. The role also supports applied AI initiatives including entity resolution, LLM-assisted analytics, embeddings, and anomaly scoring.

Leads and scales the Growth Product Insights data practice, managing and mentoring data analysts while contributing hands-on analytical work. The role drives proactive insights, experimentation, causal inference, cohort modeling, KPI development, and strategic recommendations for product and growth leaders. It also strengthens data quality and metric governance, partners with engineering teams on complex pipelines and event data, and integrates modern analytics and generative AI tools into team workflows.

Leads actuarial technology and reporting initiatives, expanding adoption of the Actuarial Technology Platform. Designs and implements process improvements for financial reporting and pricing, develops process maps, supports legacy actuarial solutions, and promotes AI-enabled analytics. Collaborates with actuarial data, enterprise analytics, and platform teams while providing technical leadership, governance, issue resolution, training, and mentorship.

Build and maintain data models, pipelines, metrics, dashboards, and analytics supporting Netflix’s advertising identity graph, entity matching, and identity resolution systems. Evaluate match quality, coverage, graph stability, and data drift; collaborate with engineering, data science, product, and identity providers; and communicate technical insights and trade-offs to technical and non-technical stakeholders.

Data Engineer II will modernize BlueLabs’ data platform by leading Kimball dimensional modeling and legacy warehouse migrations, building reliable production data pipelines, and enforcing data quality standards. The role requires advanced SQL, Python, and dbt expertise, hands-on Snowflake and Redshift experience, and ownership of testing, documentation, observability, and scalable architecture. The engineer will collaborate cross-functionally, guide Data Engineer I team members, contribute to architectural decisions, and translate business logic into maintainable, cloud-agnostic data layers.

Own analytics and reporting solutions for acquisitions, product launches, and other complex business events. Translate business changes into data models, system updates, processes, and reporting definitions. Coordinate Data Engineering, Analytics, Data Science, and Data Governance teams; lead cross-functional execution and establish a Business Intelligence Council. Partner across GTM, Finance, Product, IT, and other functions to reconcile metrics, clarify ownership, and maintain business visibility.

Lead analytics support for Fetch’s Food and Beverage vertical, serving as the primary analytics partner for Sales and client stakeholders. Own sub-vertical partnerships, manage analytic capacity, answer senior-level client questions, and guide campaign strategy. Use SQL to manipulate data, develop actionable insights, support QBR storytelling, and communicate recommendations to technical and non-technical audiences. Represent analytics cross-functionally and automate insights using AI-enabled tools.

Leads complex private banking data management and analytics initiatives, defining data strategies, quality standards, product attributes, and integrated client views. Partners with business, engineering, analytics, AI, and Wealth teams to translate needs into dashboards, KPIs, predictive models, personalized insights, and intelligent decision-making capabilities. Serves as a subject matter expert on private banking products while mentoring junior team members and enabling platform readiness.

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.

Owns B2B and B2C funnel, enrollment, revenue, and marketing performance reporting. Builds executive and Board dashboards, analyzes CAC, LTV, retention, conversion, spend efficiency, and ROI, and reconciles marketing activity with financial results. Maintains KPI definitions and reporting governance while partnering with Marketing, Finance, FP&A, Sales Operations, and Data teams. Supports strategic planning, diligence requests, and actionable business recommendations through accurate, cross-functional analytics.

Lead insurance risk analytics engagements for property and casualty carriers. Responsibilities include product demonstrations, regulatory filing guidance, pilot and proof-of-concept design, loss ratio and model validation analysis, client project management, discovery sessions, and translating insights for actuarial, underwriting, and executive audiences. The role also develops AI-assisted workflows and provides customer feedback to product teams.