Top Entry Level Data & Analytics Jobs

Build and maintain data pipelines and platform components for analytics, machine learning, and AI workloads. Implement ingestion, transformation, orchestration, testing, validation, deployment, monitoring, and troubleshooting tasks under experienced guidance. Collaborate with consultants and clients, participate in design discussions, code reviews, and agile ceremonies, document work, communicate progress, and develop foundational data engineering expertise.

Supports daily reporting by preparing recurring Excel reports, extracting data from ERP systems, cleaning and validating information, tracking metrics, responding to ad hoc requests, documenting processes, and improving reporting efficiency. The role works with Operations, Finance, Sales, and Leadership and is intended for candidates with 0–2 years of experience, including recent graduates.

Supports global ERP transformation by cleansing, validating, standardizing, migrating, and governing master data. Collaborates with stakeholders across engineering, manufacturing, supply chain, and operations to resolve data issues, establish ownership, monitor quality KPIs, maintain controls, and improve processes. The role supports IFS Cloud implementation, audit readiness, deployment, cutover, and post-go-live data integrity across global sites.

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.

Develops data acquisition strategies, integrations, migrations, workflows, architectures, and data management procedures. Builds and maintains data pipelines, ensures data quality and security, supports cloud and healthcare claims data environments, and prepares data for analytics, machine learning, and predictive modeling. Collaborates with business and technical stakeholders to define data standards, requirements, monitoring capabilities, and process improvements.

Reviews annotated sensor and map data for quality, identifies recurring issues, and recommends solutions. The role prepares reports, maintains annotation policies, develops training documentation, performs data annotation, tests internal tools, reports bugs, and creates visual assets. It also collaborates with stakeholders, partners, annotators, project managers, and engineers while supporting on-road operations and evolving program priorities.

Architects data models, curated datasets, integration patterns, and research data platforms across laboratory systems and analytical tools. Establishes data quality, lineage, metadata, catalog, and master data standards; supports LIMS and ELN architecture using platforms such as Sapio, Benchling, and Dotmatics. Leads PES roadmaps under SAFe, partners with Research IT and functional teams, mentors scientists and developers, and develops database capabilities and machine-learning data curation solutions.

Supports the development and maintenance of Qlik dashboards, reports, and data visualizations. Performs data analysis, validates reporting outputs, identifies trends and anomalies, translates business requirements into reporting solutions, troubleshoots data issues, and helps maintain metrics and KPIs. Collaborates with business stakeholders and develops knowledge of organizational data sources, business intelligence practices, and data governance standards.

Analyzes provider performance and compensation data, maintains incentive plan records, reviews contracts, processes incentive payments, supports payroll accuracy, models plan updates, answers provider questions, and assists with SOX compliance audits while meeting strict deadlines.

Build and maintain Tableau dashboards, reporting, and SQL queries while defining business metrics. Perform exploratory, statistical, forecasting, variance, and cohort analyses to support planning and business reviews. Collaborate with data engineering on pipelines, data quality, governance, and machine learning models. Use AI tools to improve analytical workflows and translate technical findings into business recommendations.