Top Remote Artificial Intelligence Data & Analytics Jobs

Designs and optimizes scalable data platforms, architectures, and production pipelines supporting AI and machine learning. Responsibilities include Databricks Lakehouse development, batch and real-time processing, data governance, security, monitoring, performance tuning, infrastructure planning, and collaboration with DevOps, MLOps, and cross-functional teams. The role serves as the AI team’s data engineering subject matter expert and ensures reliable, secure, cost-effective data solutions.

Analyze and optimize marketing campaign ROI across channels, evaluate app and partner data, conduct funnel, cohort, attribution, and retention analyses, and develop actionable recommendations. The role requires Marketing Mix Modeling, Multi-Touch Attribution, forecasting, segmentation, and statistical analysis. The consultant will collaborate with product, analytics, engineering, and marketing stakeholders to define measurement strategies, build reporting mechanisms, and translate complex data into business insights that improve adoption, retention, revenue, and campaign effectiveness.

Evaluate search engine results and provide feedback on text, audio, images, and videos to improve AI and online search experiences. Conduct online research, assess relevance and significance, maintain quality and accuracy standards, and complete assignments independently from a home office. The role requires English and Spanish proficiency, flexible part-time availability, completion of training and testing, and use of a smartphone, computer, reliable internet, and antivirus software.

Build and own scalable data architecture, canonical data models, configurable source integrations, and reliable pipelines for AI-driven financial intelligence. Use SQL, Python, dbt, cloud data warehouses, and orchestration tools to deliver idempotent, auditable data workflows. Establish lineage, data contracts, quality controls, and schema evolution practices. Lead architecture reviews, mentor distributed data engineers, and support customer-hosted multi-cloud deployments with strict tenant isolation and traceability.

Leads the data engineering team and owns platform architecture, technical roadmap, reliability, tooling, vendors, governance, security, compliance, and cost management. Remains hands-on with Snowflake, Airflow, dbt, Python, and data pipelines while coordinating delivery, mentoring engineers, managing stakeholders, and progressively assuming people-management responsibilities. Designs compliant multi-country platforms supporting underwriting, data warehousing, analytics, and regulatory requirements across Latin America and Europe.

Designs, builds, and operates large-scale Hadoop-based data pipelines and analytics platforms. Responsibilities include ingesting, transforming, and analyzing structured and unstructured data; developing Spark and streaming workloads; managing distributed systems, databases, and workflow orchestration; troubleshooting production platforms; and delivering reliable, performant, cost-effective data infrastructure for analytics, machine learning, and reporting.

Build and operate batch and streaming data pipelines on Google Cloud for analytics and machine learning. Ingest, transform, validate, and curate datasets in BigQuery; implement data quality, governance, security, monitoring, Terraform infrastructure, and CI/CD. Provide technical direction, lead code reviews, and document data processes. The role requires cloud data engineering experience, technical team leadership, strong SQL and Python skills, and eligibility for a federal Public Trust investigation.

Develop customer analytics that improve retention, growth, adoption, and operational performance. Build and maintain BI dashboards, customer health metrics, churn-risk reporting, and predictive models. Partner with Client Success, Product, Sales, and Operations to translate complex findings into actionable recommendations. The role also owns data quality, governance, reporting automation, workflow improvements, and stakeholder communication.

Design and govern end-to-end data architecture for an integrated supply-chain e-commerce platform: create data models, build scalable ETL/ELT pipelines, ensure GDPR-compliant data governance, enable real-time analytics, optimize databases, plan retention/archival, and prepare data for LLM fine-tuning and RAG use cases.

Own the analytical function for a business unit spanning growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and drive data-informed decisions and outcomes. The role requires independent data work, strong problem-solving, stakeholder influence, and end-to-end ownership of ambiguous initiatives.