Top Analytics Jobs in Lincoln, NE

Own Eve’s go-to-market data models and semantic layer for governed metrics including pipeline, conversion, CAC, win rate, ARR, and bookings. Establish certification frameworks, modeling standards, documentation, alerting, and analytical application patterns across the organization. Partner with Sales, Marketing, Customer Success, and RevOps stakeholders, resolve conflicting metric definitions, enable AI-grounded data tools, review contributions, and mentor analytics engineers.

Analyze and optimize drug pricing using data-driven models, dashboards, reports, and strategic frameworks. Partner with Sales, Operations, Marketing, Finance, Product, pharmacy partners, and data teams to identify opportunities, define success metrics, improve reporting, and balance pharmacy economics with patient affordability. Serve as a trusted analytical advisor and support backend data development and query optimization.

Supports cross-channel marketing attribution and influence modeling by documenting data sources, defining business rules, mapping and normalizing interaction data, testing reporting prototypes, and recommending implementation steps. The intern also assists with AWS marketing data organization, segmentation research, data hygiene, process documentation, and stakeholder communications. The role requires analytical thinking, thorough documentation, clear communication, and collaboration across marketing and technical teams.

Leads enterprise data governance strategy, transformation, operating model modernization, accountability, and executive engagement. Partners with senior stakeholders to improve data trust, accessibility, regulatory alignment, risk management, analytics, AI readiness, and business outcomes. Represents the Data Office in strategic and regulatory forums, establishes roadmaps and performance measures, resolves cross-functional data challenges, and drives organizational change without direct authority. The role focuses on governance and transformation rather than engineering, coding, analytics delivery, or technical administration.

Leads a client-facing healthcare analytics team that designs and maintains prior authorization decision automation programs. Consults PaaS clients, develops strategic roadmaps, translates requirements into technical blueprints, analyzes authorization and claims data, and builds KPI reports using visualization tools. Manages direct reports, improves analytics processes and AI tooling, partners with product and engineering teams, and ensures automation outcomes and client KPIs are achieved.

Designs and maintains scalable data infrastructure, ETL/ELT pipelines, canonical datasets, and AI context layers. The role improves data quality and alignment across sales and finance systems, investigates data inconsistencies, leads requirements through UAT and implementation, supports analytics self-service, and partners with cross-functional teams to deliver process improvements and reliable business data.

Own Hometap’s product and marketing event-data ecosystem, including taxonomy, tracking plans, governance, Segment Protocols, schema validation, and source management. Partner with marketing, product, and engineering to define instrumentation and event specifications. Build monitoring for schema conformance, event volume, and property completeness, while documenting standards and managing event review and deprecation processes.

Leads an eight-person analytics engineering team and sets strategy for trusted semantic models, governed data, enterprise BI, AI-powered data access, and production data applications. Oversees data modeling, evaluation datasets, telemetry, LLM routing, governance, security, and self-service capabilities. Partners cross-functionally to establish engineering standards, roadmaps, and measurable business impact while hiring, coaching, and developing team members.

Build Deepgram’s governed decision layer above warehouse gold tables. Responsibilities include modeling cross-system entities, owning semantic and metrics definitions, creating agent-facing data tools and MCP surfaces, implementing data quality assertions and reconciliation checks, evaluating AI agent accuracy, and answering business questions while leaving reusable models behind. The role requires production-grade SQL and Python, semantic-layer ownership, strong data modeling judgment, AI-native workflows, clear writing, and comfort building infrastructure from scratch.

Leads enterprise data and analytics strategy, architecture, modernization, and delivery across cloud and on-premises environments. Oversees data platforms, engineering, integration, governance, quality, lineage, security, and compliance. Partners with executives to define roadmaps and investments, manages cross-functional teams and vendors, enables AI/ML capabilities, and mentors data professionals while driving scalable, trusted data solutions and measurable business outcomes.

Manages programmatic analytics and insights for client accounts, leading measurement frameworks, brand lift studies, incrementality testing, and A/B experiments. Develops media and campaign strategies, communicates performance insights to clients, and supports pitches and QBRs. Oversees, mentors, and develops analysts while partnering with Product, Engineering, Sales, and Customer Success teams to improve measurement tools, campaign optimization, and analytical capabilities.

Designs scalable ETL/ELT pipelines, analytics-ready data models, and executive dashboards using Python, SQL, Tableau, Power BI, and cloud tools. Performs data mining, modeling, validation, documentation, and reporting while collaborating with technical and business stakeholders. Provides mentorship and technical guidance on analytics engineering, workflow automation, and data best practices. The role is fully remote, requires U.S. citizenship, and requires or permits obtaining Public Trust and Top Secret eligibility.

Designs and optimizes Power BI dashboards, semantic models, datasets, DAX measures, and reporting solutions for identity governance and administration analytics. Partners with ETL engineers and stakeholders to validate Parquet data from Azure Synapse and SailPoint sources, establish data-quality checks, monitor refreshes, define KPIs, and document data lineage. Supports compliance reporting, access governance, audit readiness, certification metrics, and Zero Trust analytics.

Lead a team delivering product and business analytics for Fetch's commerce and shoppable experiences. Own prioritization, experimentation, measurement, KPI dashboards, and stakeholder-facing insights to inform product, marketing, and commercial decisions. Manage and develop analysts while partnering cross-functionally to ensure data quality, repeatable analyses, and actionable recommendations.

Build and maintain scalable ETL/ELT pipelines, data models, integrations, and trusted datasets for analytics, reporting, dashboards, and business decision-making. Translate client requirements into technical solutions using Databricks or Microsoft Fabric, SQL, and related technologies. Implement data quality controls, governance, documentation, automation, and troubleshooting processes while partnering with business, client, analytics, and technical stakeholders.

Lead the architecture and development of enterprise analytics models, dbt transformation pipelines, semantic layers, and BI datasets. Apply dimensional modeling, data governance, quality testing, observability, and performance optimization to deliver trusted business data. Partner with executives and cross-functional teams, standardize metrics, improve legacy workflows, support AI/ML integrations, and mentor engineers. Own key domain models and contribute to the analytics platform roadmap as a technical leader.

Leads marketing and loyalty analytics strategy, measurement frameworks, forecasting, campaign reporting, experimentation, and lifecycle analysis. Partners with Marketing and Finance to connect performance metrics to business goals, automate reporting, and generate actionable insights. Builds churn, retention, segmentation, and LTV models while evaluating paid acquisition and customer growth opportunities using SQL, Python, BI tools, marketing platforms, and modern AI-enabled workflows.

Owns fraud performance analytics for depository products, including monitoring losses, approval rates, false positives, customer friction, and risk metrics. Builds data models, transformation pipelines, dashboards, scorecards, and monitoring frameworks. Conducts fraud deep dives and develops optimized rules, policies, thresholds, models, and decision strategies. Partners with Product, Engineering, Machine Learning, Data Engineering, Fraud Operations, and Risk teams to improve fraud detection and support product launches.

Own the analytics data layer by translating stakeholder needs into certified dimensional models, governed metrics, semantic layers, dashboards, and reports. Build with dbt, SQL, Databricks, Power BI, and Airflow while ensuring data quality, observability, reconciliation, documentation, and dependable orchestration. Partner closely with finance, operations, and client-facing teams, automate recurring workflows, and use AI-assisted engineering practices in a fully remote environment.

Leads Navitus’s centralized analytics team, delivering client performance analysis, financial modeling, underwriting support, pricing analysis, competitive intelligence, dashboards, and strategic recommendations. Partners with Sales, Finance, Clinical, Network Strategy, Account Management, and technical teams to support client growth, retention, RFPs, renewals, and market-facing engagements. Establishes data quality and governance standards, improves analytical processes, and mentors team members.

Leads advanced analytics across the organization, owning MAU forecasting and growth models, experimentation standards, and causal inference methods. Establishes analytical best practices, reviews high-stakes analyses, partners with data and product teams, and scales recurring work through AI tooling. Mentors data professionals and translates complex statistical findings into actionable leadership recommendations.

Build executive-facing dashboards, scalable data models, semantic layers, and reporting datasets using dbt, Looker, and SQL. Analyze large datasets, identify trends and anomalies, manage the Looker analytics platform, resolve data quality issues, and deliver analytics initiatives across business domains. Partner with product, engineering, finance, and operations stakeholders to define metrics and reporting solutions. Mentor junior analytics engineers and improve analytics standards and documentation.

Lead Client Legal Services analytics strategy and team development. Own Power BI reporting products, Microsoft Fabric data solutions, governance, predictive modeling, responsible AI adoption, ROI measurement, and executive insights. Partner cross-functionally to improve legal operations, litigation outcomes, staffing, capacity, cost management, quality, and workflow optimization while building scalable analytics capabilities and institutional knowledge.

Complete and document real-world Google Analytics workflows involving reports, traffic, users, events, conversions, audience insights, and engagement metrics. Record screen demonstrations, create prompts and rubrics, explain workflow steps, review outputs for accuracy, and follow project quality standards independently in a remote contract environment.

Lead analytics and data science initiatives to drive measurable business outcomes across various functions, including Underwriting and Claims. Oversee the full analytics lifecycle, foster stakeholder engagement, and mentor junior team members.