Top Remote Analysis Reporting Jobs in Minneapolis, MN

Develop and maintain Power BI dashboards, reports, KPIs, and business systems supporting commissioning, quality, and infrastructure projects. Gather requirements, improve workflows and data quality, coordinate user acceptance testing, manage enhancement requests, and validate reporting accuracy. Collaborate with operational, engineering, construction quality, and regional stakeholders to support governance, project readiness, issue tracking, and data-driven decision-making. Create documentation, training materials, and communications for system releases and reporting changes.

Designs, builds, and maintains Databricks data pipelines and analytics solutions using SQL and Python. Collaborates with development and IT teams, applies DevOps and CI/CD practices, supports Power BI dashboards, and contributes to data modeling, management, and governance. The role requires extensive Databricks and cloud-platform experience, strong communication skills, fluent English, and at least five years of relevant experience.

Enters, verifies, updates, and organizes information across databases, spreadsheets, and internal systems. The role identifies discrepancies, flags incomplete records, runs basic reports, maintains confidentiality, and documents recurring issues. Success is measured by accuracy, productivity, and turnaround time. Candidates need strong attention to detail, accurate typing, spreadsheet proficiency, basic computer skills, and the ability to perform repetitive work consistently.

Designs and maintains standardized healthcare reporting and analytics solutions using Azure Databricks, SQL, and Power BI. Builds data transformations, reusable models, semantic datasets, metrics, visualizations, and governed reporting products. Partners with business, clinical, pharmacy, analytics, and technology stakeholders to translate complex requirements into scalable solutions. Explores AI-enabled and conversational analytics, including Genie One, while ensuring data quality, documentation, and maintainability.

Build scalable data ingestion, transformation, and loading infrastructure using Spark, SQL, AWS, and Databricks. Develop entity resolution systems, CI/CD pipelines, anomaly detection, and high-quality data products. Design scalable processing and storage patterns, orchestration workflows, and data warehousing solutions while collaborating remotely and documenting technical designs.

Build scalable data ingestion, transformation, and loading infrastructure using Spark, SQL, AWS, and Databricks. Develop entity resolution systems, CI/CD pipelines, anomaly detection, and data quality processes. Design scalable processing systems, orchestration workflows, storage patterns, and data warehouse solutions. Collaborate remotely, document technical designs, scope projects, and solve complex data engineering and data science problems.

Create and validate complex datasets for AI benchmark tasks, including files with malformed records, missing data, truncations, and inconsistent formatting. Define correct final states, write detailed grading rubrics, and assess AI outputs for accuracy and completeness. The role requires exceptional attention to detail, error detection, format reconciliation, accurate documentation, and clear asynchronous communication in regulated or audit-sensitive contexts.

Build and scale distributed data pipelines and architectures supporting AI products and research. Responsibilities include processing large datasets with Spark, designing AWS data systems, managing SQL and NoSQL storage, writing Python and SQL, ensuring data quality and reliability, and implementing automation, orchestration, monitoring, and scalable data operations.

Annotate, label, review, and validate audio and video samples according to project guidelines. Identify and correct errors, document annotation decisions, resolve questions with project teams, meet accuracy and deadline requirements, and provide feedback to improve annotation workflows. Strong English communication, attention to detail, organization, and quality control are essential; familiarity with data-labeling tools is advantageous.

Leads customer analytics, revenue and retention reporting, executive and board reporting, renewal variance analysis, capacity modeling, customer health scoring, churn prediction, and data governance. Partners with Finance, Sales, Revenue Operations, Product, and Customer leadership to standardize SaaS metrics, improve retention, forecast revenue, and translate complex datasets into strategic recommendations and executive-ready dashboards.

Build and maintain Snowflake and dbt data models using medallion architecture, CDC ingestion, star schemas, and slowly changing dimensions. Develop governed semantic datasets for BI and AI workloads, implement testing, documentation, monitoring, alerting, row-level security, and performance tuning. Partner with product and business stakeholders to translate reporting needs into reliable multi-tenant SaaS data solutions.

Investigate and resolve data integration, file processing, and transfer issues. Monitor automated jobs, use SQL and SSIS to validate data, manage SFTP workflows, reprocess files, document integration processes, and collaborate with internal teams to ensure accurate and reliable client and vendor data exchanges.

Modernize an Azure Synapse platform and migrate workloads to Microsoft Fabric. Build metadata-driven ingestion frameworks, Fabric pipelines, Spark notebooks, validation and reconciliation processes, and Git-based CI/CD workflows. Analyze complex Synapse dependencies, implement full-load, incremental, and CDC patterns, and support medallion architecture, governance, lineage, monitoring, and security. Collaborate with client engineers through paired development, architecture discussions, code reviews, and knowledge sharing while using Claude Code for AI-assisted development.