Top Remote Analysis Reporting Jobs in Dallas, TX

Owns governance, quality, standards, and lifecycle management for eight master data domains across ERP and related systems. Administers NetSuite master data, defines validation and error-proofing controls, resolves data discrepancies, maintains audit-ready documentation, and reports quality KPIs. Partners with IT, Supply Chain, Finance, Commercial, Regulatory, Customer Care, and other teams on integrations, migrations, product launches, compliance, serialization, and pricing. Provides cross-functional guidance and drives data discipline without direct authority.

Leads competitive intelligence and market analysis for Medicaid, DSNP, and Marketplace RFPs. Reviews competitor responses, identifies gaps and differentiators, develops recommendations, maintains intelligence databases, supports capture strategy and oral presentations, and advises business development, product, legal, and executive stakeholders. Provides benchmarking, proposal analytics, training, and protest support while refining RFP methodologies. Requires 50% or more travel.

Builds and manages clinical outcomes and client experience measurement programs. Responsibilities include administering surveys, consolidating data, defining measurement methodologies, developing scorecards and recurring reports, analyzing trends, and connecting outcomes to provider network performance. The role translates complex findings into actionable recommendations for clinical and operational leaders, supports quality improvement initiatives, and contributes to research projects, white papers, and thought leadership.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

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 solutions; managing distributed systems; supporting machine learning and reporting workloads; and ensuring reliable, performant production operations. The role requires expertise across Hadoop, Spark, SQL, orchestration, cloud data platforms, troubleshooting, and data engineering best practices.

Conducts operations research and advanced analytics to optimize public-sector command performance and reduce costs. Develops optimization, statistical, simulation, regression, mathematical, and decision-analysis models; implements analytical models in Python; performs data analysis; supports software development; assesses operational questions; and prepares decision briefings, reports, presentations, and recommendations for senior leadership.

Reviews and evaluates domain-specific documents, providing structured feedback on quality, accuracy, and relevance. The role requires strong analytical and written communication skills, detailed document review, proficiency with spreadsheet and presentation software, and the ability to follow structured guidelines. Candidates must have a bachelor’s degree, at least three years of relevant professional experience, English proficiency, and reside in the United States, Europe, the United Kingdom, or Canada.

Lead the design and operation of a scalable data platform, owning ingestion, orchestration, observability, lineage, data modeling, contracts, and ETL/ELT pipelines. Build Databricks and PostgreSQL solutions, implement data-quality controls, anomaly detection, governance, privacy, and compliance practices, and establish reusable engineering standards. Mentor engineers and ensure reliable, traceable data across markets and product lines.

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.

Analyze and manipulate large datasets supporting energy modeling and SEM programs. Review data before and after modeling, maintain standardized templates and databases, integrate data into statistical software, quality-control models and reports, and create work instructions. The role also involves automating data processing, troubleshooting Visual Basic and Excel tools, managing Smartsheet data, supporting benchmarking reports, and collaborating with internal teams and occasional clients.