Top Remote Data Analyst Jobs

Lead enterprise data governance by defining metadata, CDEs, data quality rules, and catalog/lineage. Partner with stewards, owners, IT and business stakeholders to monitor data quality, automate governance workflows, certify reports, and implement governance tooling (e.g., Collibra). Ensure PII handling, document processes, train users, and drive continuous improvement.

Lead Data QA for the Enterprise Data Warehouse: define QA strategy, design and execute source-to-target validations, CDC and ETL checks, data masking verification, build quality dashboards and automations, mentor QA analysts, manage defects and release readiness, and collaborate with Data Engineering, BI, Product, and stakeholders to ensure data is accurate, complete, and audit-ready.

The Junior Data Analyst will conduct qualitative and quantitative analysis, evaluate processes, and provide scheduling and documentation support for a DHS client.

Support settlement programs by answering client data questions, validating claimant data and calculations using SQL/Python, performing quality checks, documenting solutions, and collaborating with litigation, engineering, and product teams.

As a Senior Data Analyst, you will analyze game telemetry data, translate business needs into insights, and guide technical teams. You will also lead exploratory data analysis and maintain data quality practices.

Join Oliver Wyman's Data & Analytics team to design and implement analytic solutions and prototypes for clients across industries. Work on NLP, ML, and GenAI projects, build models (e.g., PD, loss models) in Python/R, validate AI chatbots, and translate complex analytics for client decision-making while collaborating with global teams.

Analyze Construction & Design Services project data to produce reports, dashboards, and predictive/prescriptive models. Build data pipelines and models, mine large datasets, collaborate with IT and stakeholders, support data governance, respond to ad-hoc requests, and improve decision-making through visualization and analytics.

Perform end-to-end data migrations for banks and credit unions: analyze donor systems, define requirements, map and transform data with SQL, validate conversions, reconcile results, resolve data issues, and build reusable automation and AI-assisted tools to improve migration speed and accuracy while supporting multiple client engagements.

Lead marketing measurement: own attribution, incrementality testing, and media mix modeling. Design and analyze experiments, build repeatable templates and self-serve analytics, translate results into budget and growth recommendations, ensure data integrity, and embed AI tools into analysis workflows to measure pipeline and revenue impact.

Lead advanced analytics and predictive modeling for readiness-based spares and sustainment decisions. Translate business problems into analytical frameworks, build predictive/diagnostic models, document data pipelines, mentor junior analysts, and collaborate with engineering and program stakeholders to support Navy and Department of War programs.

Own and build audit-ready data architecture, ETL pipelines, and validations to support internal audit. Maintain inventories, playbooks, and catalogs; assess downstream impacts of schema changes; implement automated data quality checks and monitoring; and translate audit/business requirements into technical specifications for stakeholders.

The Director Analyst role involves creating research and engaging clients on Data Quality, providing actionable insights, and mentoring junior team members. They analyze market trends and support clients in improving data quality initiatives.

The Data Product Analyst designs and manages data products for insurance domains, ensuring they meet analytical and compliance standards while collaborating with cross-functional teams.

The Data Analyst will analyze marketing and ecommerce data, build reporting, dashboards, and KPIs, and enhance marketing attribution modeling.

The Senior Data Analyst will lead complex data analysis projects, mentor junior analysts, develop dashboards, and provide insights to drive strategic decisions. Responsibilities include troubleshooting data issues and communicating findings effectively.

As a Data & Analytics Analyst, you'll collaborate with clients to create tailored solutions, implement prototypes, and share ideas to solve analytical challenges.

Analyze customer and caregiver data to identify workflow improvements, build dashboards and visualizations, support Customer Success with QBRs and presentations, translate findings into actionable recommendations, and create scalable data processes while advising Product and Engineering.

Lead governance, standardization, and quality of enterprise location master data. Analyze data flows, perform root cause analysis, define KPIs, drive data quality initiatives, document processes, and support stakeholder training to improve reporting and operational decision-making.

Extract, clean, and analyze data from multiple sources, maintain ETL pipelines and data quality, build dashboards and reports, and partner with IT and business teams to deliver actionable insights.

Research, evaluate, and manage partnerships with data providers. Assess datasets for coverage and interoperability, support deal structuring, maintain partnership pipeline, and collaborate with product, engineering, and legal to integrate data into the platform.

As a Senior Data Analyst, you will analyze data, create reports, manage data pipelines, and provide insights to influence product decisions. You will collaborate with various teams and perform A/B testing.

Audit and review data labeling outputs, provide actionable feedback, train and support labeling teams, monitor workflows, identify recurring issues and edge cases, participate in calibrations and stakeholder syncs, and help improve guidelines and processes in human-in-the-loop AI workflows.

Audit and ensure quality of data labeling outputs, coach and onboard Data Labeling Associates, provide actionable feedback, monitor workflows, identify patterns and edge cases, and help improve guidelines and processes in human-in-the-loop AI workflows.

Audit and validate data labeling outputs, provide structured feedback and hands-on training to annotators, monitor workflows and escalate issues, help apply and improve guidelines, and identify patterns in human and model outputs to maintain high-quality AI training data.

Audit and ensure quality of labeled data by reviewing DLA outputs, providing feedback and coaching, supporting onboarding and training, monitoring workflows, identifying recurring issues and edge cases, and contributing to guideline and process improvements in human-in-the-loop AI workflows.