Top Remote Analysis Reporting Jobs in Peoria, IL

The Business Intelligence Engineer manages enterprise data sources, supports data quality and governance, and translates transactional data into KPIs for business teams. Responsibilities include guiding analyst teams, handling data inquiries, supporting advanced analytics, monitoring data performance, defining data requirements with stakeholders, and presenting findings to technical and non-technical audiences. The role requires SQL, Tableau, relational database, and data modeling or analytics expertise.

Own Step’s analytics data models, transformation pipelines, metric definitions, semantic layer, and data quality practices. Partner with Product, Marketing, Engineering, Risk, Growth, and Finance to establish trustworthy data foundations and enable self-service analysis. Build reusable query, visualization, and code components; apply AI-augmented workflows; and collaborate with data scientists, analysts, and platform engineers. The role requires strong SQL, modeling, pipeline, BI, communication, and independent ownership skills, with occasional travel for team onsites.

Conducts econometric, causal, statistical, and optimization analyses to improve operational efficiency, resource allocation, product performance, and business outcomes. The role defines efficiency measures, develops forecasting and optimization tools, partners with product and engineering teams on data instrumentation and implementation, and communicates findings to executives through clear reports.

Leads the design, development, governance, and productization of enterprise data models, pipelines, data products, and ontologies. Builds machine-learning-ready datasets, implements security and lineage controls, optimizes Databricks and Delta Lake environments, establishes data-product standards, collaborates with architects and data scientists, and mentors engineering talent. The role also defines product roadmaps, service levels, usage metrics, and continuous-improvement processes for enterprise data assets supporting AI agents, analytics, and applications.

Owns lead and hotel account data quality for a sales organization. Responsibilities include CRM audits, deduplication, enrichment, lead qualification, routing, SLA development, escalation resolution, data workflow documentation, vendor evaluation, SQL-based validation, and responsible use of AI for quality assurance and anomaly detection.

Owns a six-month analytics engineering engagement covering scalable Snowflake architecture, marketing and sales ingestion, finance automation with LLM agents, data reconciliation, and governed Tableau reporting. Builds production-ready pipelines, data models, quality monitoring, human-in-the-loop controls, and row- and column-level security. Delivers documentation and knowledge transfer while coordinating independently with finance and publishing stakeholders.

Analyzes and interprets data, evaluates results, and provides actionable insights to support strategic decision-making across departments.

Designs secure, scalable database and data-warehouse architectures; develops logical and physical data models, schemas, metadata, interfaces, and integration standards. Coordinates clustering, backup, recovery, load balancing, and availability strategies. Plans upgrades, recommends infrastructure and configuration improvements, and tests database structures and programs. Collaborates with architects, analysts, and project teams to align database solutions with business and technical requirements.

Own Mux’s end-to-end data platform, including ingestion, Snowflake administration, dbt transformations, orchestration, data quality, observability, and reverse ETL. Build semantic models supporting AI agents, establish governance and reliability standards, and partner with Engineering, Finance, Product, and GTM. Use AI coding tools to develop and maintain pipelines while validating output quality. The role requires strong SQL, Python, data modeling, cloud warehouse administration, and production data engineering experience.

Leads secure, scalable customer data architecture across Adobe Experience Platform and cloud environments. Designs data strategies, models, governance frameworks, ingestion and integration solutions, migrations, and real-time customer profiles. Partners with data engineers, Adobe, and clients to address data quality, security, privacy, performance, and regulatory requirements. Uses SQL, scripting, APIs, and Postman to support implementation and automation while providing technical leadership across multiple customer projects.