Top Remote Data & Analytics Jobs in Kansas City, MO

Designs, develops, and maintains scalable ETL pipelines, data lakes, warehouses, and integrated data solutions. The role includes requirements analysis, Agile collaboration, testing, debugging, documentation, cloud data engineering, and optimization of reliable and performant systems. It requires strong Python, SQL, database, distributed processing, data engineering, software development, and cloud platform skills, along with effective communication and problem-solving abilities.

Processes and validates financial, payroll, ledger, time-tracking, expense, project, and operational data. Converts information into accurate workbooks, templates, reports, and organized deliverables while maintaining employee, project, and expense records. Researches relevant company and industry information, organizes audit documentation, supports interview qualification summaries, and manages multiple data requests and deadlines in a remote environment.

Reviews and analyzes large business datasets for accuracy, completeness, relevance, privacy, and quality. Conducts document reviews involving PII, identifies data issues and anomalies, provides feedback to support AI training, refines quality assurance standards, and communicates findings to project stakeholders while maintaining data privacy and security compliance.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

Leads supply chain analytics, reporting, automation, and Power BI solutions while managing product readiness portfolios for launches and product changes. Coordinates cross-functional deliverables, governance forums, SKU readiness, risk escalation, documentation, and milestone tracking across commercialization projects. Translates business needs into scalable analytics and digital solutions, communicates insights, and drives stakeholder adoption and operational improvements.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

Designs and leads enterprise data architecture, including warehouses, lakes, lakehouses, data models, integration pipelines, governance, security, and analytics platforms. Partners with engineering, analytics, application, and business teams to create scalable cloud data ecosystems. Responsibilities include technology evaluation, migration and modernization strategy, architecture reviews, documentation, data quality, lineage, metadata, and technical mentorship.

Analyze large healthcare datasets to extract insights, build data models, reports and dashboards, ensure data quality, collaborate with cross-functional teams, and present findings to stakeholders.

Analyze business data, build and maintain business models, provide actionable insights and reports, collaborate with cross-functional teams, monitor performance, and recommend efficiency improvements to support strategic decision-making.

Analyze large healthcare datasets, build and implement quantitative models, perform statistical analyses, ensure data accuracy, collaborate with cross-functional teams, and provide actionable insights to improve healthcare delivery.

Analyzes, integrates, and visualizes complex datasets to support enterprise strategy and continuous improvement. Develops Snowflake and Azure data solutions, gathers and documents requirements, writes Agile user stories, coordinates business and IT stakeholders, and ensures data quality through process documentation, root-cause analysis, testing, and incident support. Works independently while influencing departmental strategy and may provide occasional production support during nights or weekends.

Lead fraud analytics for application fraud detection: identify emerging patterns, design and test real-time defenses, balance risk and customer experience, build dashboards and metrics, and collaborate with Operations, Product, Data Science, and Engineering to reduce fraud losses and improve cardholder experience.