Top Remote Data & Analytics Jobs

Analyzes operational and management problems using mathematical and simulation models, statistical testing, and validated data. Develops, verifies, and evaluates models; prepares technical reports and briefings; conducts data elicitation and project-scoping sessions; supports implementation assessments and customer engagement. May lead small project teams, develop client training, and support related projects.

Conduct forensic analytics for anti-money laundering and financial crime monitoring. Analyze large transactional datasets with SQL and Alteryx, identify suspicious behaviors and emerging typologies, develop and validate detection scenarios, calibrate thresholds, perform lookbacks and segmentation studies, evaluate transaction networks, and support AML investigations, SAR narratives, and regulatory responses.

Own and evolve Worldly’s production data lake, Postgres warehouse, CDC and streaming pipelines, dbt transformations, orchestration, BI access controls, and graph integrations. Maintain reliable, governed analytics infrastructure using Iceberg, Trino, Dagster, Terraform, and AWS. Support production generative AI workflows involving embeddings, similarity search, extraction, and classification. Responsibilities include data quality, monitoring, security, incident response, disaster recovery, schema governance, and collaboration with data science and analytics stakeholders.

Designs, builds, and optimizes cloud data solutions using Microsoft Fabric and Azure. Develops data pipelines, Lakehouse architectures, ELT/ETL processes, data models, semantic models, and Power BI reporting. Partners with stakeholders to define requirements and technical designs, supports platform migrations, and implements data quality, governance, security, and monitoring practices. The role also contributes to AI and machine learning data initiatives and requires strong SQL, Python, PySpark, and modern cloud data engineering expertise.

Design and lead scalable, secure, highly available database infrastructure for multi-terabyte SQL Server environments. Set technical direction for data architecture, performance, real-time pipelines, security, and growth. Troubleshoot complex production issues, optimize queries and indexes, guide cross-functional projects, mentor engineers, review changes, reduce technical debt, and evaluate emerging technologies. Monitor database health using Splunk, Datadog, and Grafana while influencing engineering and Site Reliability strategy.

Designs and implements scalable data architectures, ingestion and transformation pipelines, API integrations, cloud data solutions, and data governance practices. The role supports analytics, business intelligence, dashboards, and AI models while optimizing infrastructure performance and cost. Responsibilities include data modeling, security, quality, lineage, compliance, documentation, and collaboration with cross-functional stakeholders. Requires expertise in Microsoft Fabric, AWS and Azure data services, SQL, Python, Tableau, and modern ETL/ELT practices.

Own and scale the company’s analytics infrastructure, including BigQuery data warehouses, dbt data marts, external data exchange, DataOps, data quality, governance, security, and cloud cost optimization. Partner with application database teams, analytics engineers, and data scientists to enable reliable metrics and scalable client data delivery.

Performs geospatial data management, analysis, visualization, and analytical modeling for government and commercial clients. Supports hazard identification, risk assessment, and risk reduction efforts using ArcGIS and Microsoft tools. Develops web, mobile, and desktop applications; tests, deploys, documents, and maintains solutions. This non-full-time, on-call role requires frequent travel and deployment to disaster recovery sites nationwide.

Manage end-to-end data operations, including collection, processing, delivery, pipeline optimization, and data quality monitoring. Collaborate with engineering, product, and operations teams to improve data workflows, identify inefficiencies, and implement scalable solutions. Analyze data to provide actionable insights for decision-making and AI model development while communicating technical issues to non-technical stakeholders.

Designs, builds, and maintains scalable data platforms and resilient pipelines using Medallion architecture, Databricks, Apache Spark, Parquet, Iceberg, and Delta Lake. The role includes database optimization for PostgreSQL and SQLite, data quality and performance monitoring, ETL/ELT development, troubleshooting, documentation, and collaboration with analytics stakeholders.