Top Entry Level Data & Analytics Jobs in Lincoln, NE

Designs and maintains scalable data infrastructure, Snowflake databases, data models, and automated ELT pipelines. Extracts, cleans, validates, and transforms data from databases, APIs, and files while ensuring data quality and integrity. Troubleshoots workflow failures, optimizes queries and pipelines, and collaborates with analysts, product owners, developers, and other engineers. The role may require after-hours ownership of critical data pipelines.

Annotate Korean PDF pages by identifying, bounding, typing, and ordering document regions; link related components; transcribe Hangul, hanja, and handwriting exactly; capture page metadata; and flag illegible or unsuitable pages. Experienced annotators also review colleagues’ work. The role requires exceptional character-level accuracy, consistent taxonomy application, and comfort with complex layouts such as tables, diagrams, newspapers, exams, and handwritten forms.

Annotate Japanese PDF pages by identifying, bounding, typing, and ordering document regions; link related components; transcribe all text exactly, including handwritten and vertically arranged content; capture metadata; and flag illegible or unsuitable pages. Experienced annotators also review colleagues’ work. Native Japanese fluency and command of kanji, hiragana, katakana, furigana, and variant characters are essential.

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.

Reviews, develops, validates, and refines dashboards, reports, visualizations, calculations, data sources, KPIs, and analytical workflows across BI platforms. The role documents findings, provides structured feedback, troubleshoots reporting accuracy, and evaluates AI-powered agents and workplace connectors used in analytics processes.

Organise and curate large amounts of raw material: extract key points from transcripts, collate and maintain clean spreadsheets, tag and categorise links and documents, and create findable information structures following or suggesting systems.

Own production data ingestion pipelines for healthcare and benefits data, including normalization, mapping, validation, reconciliation, observability, and secure handling of sensitive information. Unify census data processes, scale the platform, improve identity matching, and build automation and TypeScript tooling. The role requires strong Python and SQL skills, production orchestration experience, reliable testing and data-quality practices, and effective communication with technical and nontechnical stakeholders.

Manage end-to-end data operations, including collection, processing, delivery, pipeline optimization, and data quality monitoring. Collaborate with engineering, product, and operations teams to improve data workflows, identify inefficiencies, and implement scalable solutions. Analyze data to provide actionable insights for decision-making and AI model development while communicating technical issues to non-technical stakeholders.

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.

Manage MSSQL and PostgreSQL databases, including installation, configuration, monitoring, performance tuning, security, backup and recovery. Develop optimized SQL queries, stored procedures, triggers, and views. Use AWS services for database deployment and scaling, Git/GitHub for version control, and collaborate with developers and system administrators. Support data warehouse design, ETL processes, reporting requirements, and database troubleshooting.

Build and maintain production-grade dbt data models and pipelines across ingestion, transformation, and visualization. Develop reliable datasets supporting reporting, analysis, finance, revenue recognition, and company decision-making. Collaborate with Finance, Engineering, Sales, Product, and Marketing to understand data needs, improve manual workflows, validate data accuracy, and create scalable solutions using modern data tools.