Top Remote Data & Analytics Jobs

Design and implement end-to-end AI features, scalable data pipelines, ETL/ELT workflows, Generative AI and agentic systems, ML and BI solutions, and MLOps/LLMOps practices across AWS, Azure, and GCP. Apply RAG, embeddings, vector databases, security governance, evaluation, reliability, and guardrails. Own technical workstreams, mentor junior engineers, and promote software engineering best practices in consulting environments.

Leads Curology’s enterprise data strategy, infrastructure, architecture, customer data platform, analytics, experimentation, and data science functions. Builds scalable pipelines and governance, develops predictive and prescriptive models, advances experimentation, establishes executive dashboards and KPI frameworks, and ensures data quality, security, PII compliance, and responsible AI use. Partners with Product, Engineering, Marketing, Clinical, Finance, and executive leadership while scaling a high-performing analytics and data science team.

Leads analytics delivery across multiple clients, combining marketing data to evaluate performance, optimize investments, and inform growth decisions. Designs and implements advanced measurement approaches including attribution, incrementality testing, experimentation, and full-funnel measurement. Develops measurement frameworks, communicates insights to clients and senior stakeholders, partners with technical and marketing teams, supports new business, and mentors junior analysts.

Build analytical frameworks, measurement methodologies, and decision support to drive enterprise decisions across marketing, loyalty, pricing, menu, media, and operations. Mine and integrate customer, transaction, digital, and operational data; develop segmentation, predictive models, and dashboards; lead test-and-learn, incrementality, and forecasting efforts; translate analysis into clear business recommendations and scalable decisioning tools.

Design and scale Zip’s enterprise data and AI platform across batch, streaming, structured, and unstructured workloads. Build reliable pipelines, AI-ready data products, semantic models, vector and hybrid search, and RAG capabilities. Establish standards for modeling, testing, governance, security, observability, and automation. Partner with engineering, data science, product, and business teams while mentoring engineers and influencing platform architecture.

Provides actuarial leadership for AI-enabled disability claims models across LTD and STD claim lifecycles. Designs, validates, calibrates, monitors, and interprets models; connects outputs to loss ratios, reserves, claim duration, return-to-work outcomes, and intervention effectiveness. Partners with claims, data science, technology, finance, and vendors on model governance, deployment, reporting, and operational adoption. Communicates model performance, limitations, financial impact, and recommendations to technical and business leaders.

Lead enterprise data architecture across multiple SaaS products by unifying reporting platforms, data models, and analytics capabilities. Assess current systems, define target-state architecture, establish data governance and quality standards, separate analytical workloads from transactional systems, and develop phased migration roadmaps. Guide database, engineering, and BI teams on cloud platforms, integration, modeling, performance, security, and reliability. Enable trusted cross-product reporting, governed self-service analytics, and data foundations for AI and advanced analytics.

Design and build modern GCP lakehouse architectures, including Python and Spark pipelines, BigQuery ingestion, Kafka CDC, Delta Lake and Iceberg UniForm integrations, Delta Sharing endpoints, Snowflake and Databricks connectivity, data lineage, governed access controls, and semantic-layer alignment. Collaborate cross-functionally, troubleshoot full-stack issues, and deliver reusable data-sharing adapters and end-to-end data features.

Build and lead complex data integration projects from client onboarding through ongoing platform usage. Analyze and resolve data discrepancies, troubleshoot SIS and SSO integrations, develop internal Retool applications, perform code reviews, prioritize backlogs, manage projects, and mentor teammates. The role requires advanced SQL, development expertise, technical leadership, and collaboration with internal stakeholders, with Ruby on Rails, AWS, Git, and K-12 education experience preferred or desired.

Designs, develops, and optimizes enterprise data platforms, ETL workflows, databases, reporting solutions, and dashboards. Leads technical designs, SDLC processes, troubleshooting, documentation, and production support while collaborating with stakeholders and vendors. Uses SQL Server, SSIS, T-SQL, Microsoft BI tools, and DevOps technologies to build scalable, reliable data integration and business intelligence solutions. Supports cloud migration and may use Azure, Fabric, Snowflake, or Databricks.