Top Remote Artificial Intelligence Data & Analytics Jobs

The role involves administering Salesforce, developing automations with Flows and Apex, and maintaining data pipelines to support revenue operations at ElevenLabs.

Develops and optimizes enterprise database environments across SQL Server and Azure platforms. Responsibilities include query and execution-plan tuning, indexing, database design, monitoring, incident response, backup and recovery, high availability, security, compliance, capacity planning, automation, documentation, and collaboration with development and DBA teams. The role supports ETL/ELT, data warehouses, lakehouses, and large-scale datasets, participates in on-call support, and may travel up to 10%.

Supports real-time media monitoring, stakeholder insight analysis, data preparation, alerts, briefings, and client reports. Responsibilities include tracking news across platforms, creating Excel pivot tables, updating charts and graphics, interpreting reputation-risk scoring, conducting research, and translating data into clear narratives. The intern collaborates with analysts to identify trends and emerging risks while developing analytical, writing, and business intelligence skills.

Leads data engineering and analytics strategy across pharmaceutical technical operations. Builds data pipelines, improves manufacturing data quality and accessibility, translates business and manufacturing needs into scalable analytical solutions, and drives adoption of process monitoring, manufacturing process intelligence, and data-driven decision-making. Partners across technical operations, manufacturing, digital, IT, and site teams to prioritize use cases, ensure execution, establish governance, and deliver measurable business value.

Own solution design, implementation, and debugging for Hightouch’s Identity Resolution product. Work directly with customer data architectures, configure identity models, write SQL against data warehouses, investigate data discrepancies, explain system behavior, and surface product gaps. Partner with sales engineering, support, technical account management, and executives to deliver customer solutions and advise on data strategy. Use coding agents while reviewing implementation quality and maintaining best practices.

Leads the architecture, design, and implementation of scalable GCP and Databricks data lakehouse platforms. Designs real-time and batch pipelines, data models, governance, cataloging, security, and performance optimization. Advises clients, translates business requirements into technical solutions, leads project teams, mentors engineers, reviews architectures, and balances strategic planning with hands-on proof-of-concept and troubleshooting work.

Build and maintain large-scale AWS ETL and data-processing pipelines handling billions of records. Investigate data-quality issues, analyze scoring outcomes, optimize query and processing performance, integrate diverse data sources, and develop reliable datasets. Own architecture and projects end to end, including testing, validation, production troubleshooting, and documentation. Apply statistical, machine-learning, AI, and analytical methods to improve data accuracy, scalability, and product outcomes with limited oversight.

Build and maintain scalable ELT pipelines, dbt models, semantic layers, and governed product analytics assets. Partner with Data Engineering, Product Engineering, Data Governance, analysts, and product managers to define trusted metrics, monitor data quality, document taxonomies, and support self-service analytics. Translate product usage telemetry into certified metrics for expansion and upsell analysis, while communicating technical concepts to technical and non-technical stakeholders and driving engineering best practices.

Leads enterprise business intelligence strategy, roadmap, governance, analytics, data engineering, data science, and AI-enabled automation. Partners with executives across functions to define KPIs, deliver dashboards and predictive insights, oversee data platforms and integrations, manage technology vendors and budgets, and lead a global team of BI engineers, data engineers, analysts, and data scientists.

Own marketing channel performance tracking, reporting, experimentation, dashboards, forecasting, and LTV analysis. Develop measurement frameworks, identify data issues, and translate complex findings into actionable recommendations for Marketing, Growth, Product, and Finance stakeholders. The role requires strong SQL, BI dashboarding, marketing measurement, experimentation knowledge, and effective analytical communication, with AI tools used to accelerate reporting and analysis.