Top Remote Analysis Reporting Jobs in Kansas City, MO

Serves as an enterprise architecture subject matter expert within a DoD environment. Supports DoD Business Enterprise Architecture initiatives, business process alignment, enterprise strategy, stakeholder analysis, requirements documentation, reporting, and project activities using MEGA HOPEX and Microsoft Office tools.

The Analytics Engineer will analyze sales proof-of-concepts and self-service trials, support growth and product-led sales initiatives, and evaluate pricing and monetization strategies. The role partners cross-functionally, models data in dbt, maintains data warehouses and ELT pipelines, experiments with emerging data and AI tools, and trains teammates on effective data workspace practices.

Extract, transform, validate, and load client and Enverus data into MineralSoft using AI-enabled extraction tools and ETL processes. Configure mappings, run queries and reports, troubleshoot integrations with EnergyLink, and ensure data quality and completeness for internal and client use.

Leads and develops a Data Platform engineering team while owning delivery of complex data initiatives. The role establishes delivery discipline, assesses technical and implementation risks, partners cross-functionally, and drives production reliability, data quality, observability, incident management, and on-call readiness. It also guides the evolution of scalable data ingestion, transformation, lakehouse, cloud architecture, governance, and reusable platform capabilities.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

Own GTM performance analytics, KPI and target setting, reporting, forecasting, compensation planning, and performance reviews across Sales and Post-Sales. Identify anomalies and optimization opportunities involving conversion, churn, productivity, pricing, packaging, segmentation, channel economics, and customer quality. Partner with Finance and GTM leaders to develop recommendations, lead strategic initiatives, and ensure data integrity while translating analysis into actionable business decisions.

Designs and operates batch, streaming, and real-time data platforms and pipelines using Spark, Kafka, Iceberg, Airflow, and cloud infrastructure. Owns data lake evolution, data quality, observability, reliability, governance, event instrumentation, schema management, and privacy controls for PII, retention, deletion, and residency. Leads complex initiatives end-to-end, troubleshoots production issues, mentors engineers, and develops AI agent harnesses that enforce data engineering standards and quality gates.

Build and operate production-grade ELT pipelines from product, Salesforce, and third-party systems into Snowflake using dbt. Ensure testing, monitoring, schema versioning, governance, privacy, access controls, data quality, cost, and performance. Support AI initiatives with trusted data, contribute to warehouse and semantic-layer architecture, and translate cross-functional stakeholder needs into scalable data solutions.

As the first in-house Data Engineer, you will own the data infrastructure, build and maintain data pipelines, and collaborate with teams to ensure quality data access and insights.

Lead a GTM Analytics team supporting Marketing, Sales, RevOps, and Customer Operations. Manage embedded analysts, establish trusted reporting and QA processes, review SQL and dashboards, guide funnel, campaign, sales, support, forecasting, segmentation, attribution, and experimentation analysis, and deliver executive recommendations. Partner with GTM leaders and data engineers, coach analysts, and develop self-service analytics, automation, governed dashboards, metric documentation, and AI-assisted workflows.