Top Remote Data & Analytics Jobs in San Antonio, TX

Build and maintain scalable batch and incremental data pipelines using Python, SQL, and Snowflake. Develop data warehouse models, transformations, automation, quality checks, lineage, alerting, and performance optimizations. Collaborate with analysts, data scientists, and application teams to deliver trusted datasets, while documenting pipelines, schemas, and data contracts.

Own the technical design and operation of Palmetto’s governed data platform. Build trusted data products, semantic models, ingestion and transformation pipelines, secure access interfaces, and agent-ready data services. Establish governance, masking, authorization, observability, CI/CD, and audit readiness. Partner with finance, operations, product, and engineering teams while mentoring engineers and setting cross-team technical direction.

Analyze product funnels and user behavior to identify growth opportunities, improve customer and business outcomes, and guide product decisions. Partner with product, design, engineering, and analytics teams to track hypotheses, report key metrics, reconcile data discrepancies, maintain analytics and experimentation catalogs, and communicate actionable insights. The role requires strong SQL, visualization, experimentation, and cross-functional communication skills in a fintech or e-commerce environment.

Complete and document real-world Google Analytics workflows involving reports, traffic, users, events, conversions, audience insights, and engagement metrics. Record screen demonstrations, create prompts and rubrics, explain workflow steps, review outputs for accuracy, and follow project quality standards independently in a remote contract environment.

Build, test, and maintain batch and event-driven data pipelines and ETL/ELT workflows. Transform and validate large datasets, troubleshoot data-quality issues, improve workflow reliability, and support platform modernization. Collaborate with engineering, product, and data stakeholders while following strong practices for version control, testing, monitoring, documentation, and code review. The role may occasionally require work outside standard business hours.

Design and implement attribution, source tracking, CRM integrations, ROI measurement, funnel reporting, and sales performance systems for B2B clients. Consult with revenue leaders, define metrics, build and validate reporting across marketing, sales, CRM, advertising, and BI platforms, troubleshoot data quality issues, document solutions, and train client teams. Contribute to implementation methodologies, technical content, automation, and AI-enabled data validation and analysis.

Reviews and annotates video content by identifying objects, people, actions, events, and visual details for AI training. Follows project guidelines, checks annotation quality, corrects inconsistencies, flags unclear content, meets productivity targets, and adapts to updated requirements. The role requires independent, detail-focused work, strong written English comprehension, basic computer skills, reliable internet, and personally provided equipment suitable for video annotation.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

Build and maintain scalable batch and streaming data pipelines, analytics warehouses, transformation layers, and dimensional data models. Perform SQL-based analysis, support dashboards and reporting, monitor data quality and delivery SLAs, troubleshoot incidents, and improve operational reliability. Collaborate with engineers, analysts, data scientists, and business stakeholders while contributing to documentation, governance, observability, and automation.

Build scalable data ingestion, transformation, and loading infrastructure using Spark, SQL, AWS, and Databricks. Develop entity resolution systems, CI/CD pipelines, anomaly detection, and high-quality data products. Design scalable processing and storage patterns, orchestration workflows, and data warehousing solutions while collaborating remotely and documenting technical designs.