Top Remote Data & Analytics Jobs in San Antonio, TX

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.

Leads the migration of legacy data warehouses and pipelines to an Azure and Databricks lakehouse. Defines technical architecture, reusable Azure Data Factory and Databricks pipelines, reconciliation methods, quality gates, and performance solutions. Mentors data engineers, reviews technical work, and collaborates with product, QA, platform, and customers in a remote Agile environment.

Delivers daily, weekly, and month-end customer reporting while maintaining solution components, validating data, and resolving reporting issues. Analyzes complex datasets, supports customer implementations, and improves reporting efficiency using Excel, Access, SQL, and the Infios FAP Portal. Collaborates with Operations, Product, Implementations, Commercial teams, and customers to provide accurate, data-driven outputs and document scalable processes.

Design and maintain Twilio’s R&D analytics data layer using AWS and analytics modeling tools. Define metrics and data lineage, build automated reconciliation and quality checks, investigate anomalies, and communicate solutions. Collaborate with Product and Engineering, mentor analytics team members, and establish best practices for data modeling, testing, documentation, and code review.

Analyze case, client, and financial data to build Power BI dashboards, executive and client reporting, recurring Excel deliverables, SQL analyses, and lightweight automations. The role investigates discrepancies, supports ad hoc business questions, documents metric definitions, and partners with operations, sales, finance, and leadership to improve reporting and reduce manual work.

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.

Build and maintain dimensional data models in dbt and Snowflake for finance, operations, product, sales, and other business functions. Translate business questions into reliable data solutions, own model lifecycles, establish modeling and testing standards, optimize Snowflake performance and costs, collaborate with data engineering teams, review code, mentor colleagues, and communicate data definitions and tradeoffs to business leaders.

Owns the CX Operations data foundation in Snowflake by designing architecture, governance, data quality standards, and reusable dbt models. Leads complex analyses, dashboards, scenario planning, and executive communication while mentoring analysts and partnering with engineering and strategy teams. Supports AI enablement by creating trusted data models, evaluation standards, and agent performance tracking.

Build and maintain SQL and dbt data models, tables, views, dictionaries, and self-service tools on BigQuery. Support Accounting, Actuarial, and Claims stakeholders from request through production, monitor data quality, prevent defects, document processes, and review AI-generated code. The role requires hands-on data development, collaboration across projects, and growing ownership of a data domain and stakeholder relationships.

Build and maintain dbt transformation models and Power BI semantic layers on Microsoft Fabric. Establish data quality testing, observability, governance, CI/CD, documentation, and access controls. Partner with business and technology stakeholders to define reliable metrics and data products, optimize performance and costs, troubleshoot issues, and mentor analysts and engineers while advancing analytics engineering standards.