Top Entry Level Data & Analytics Jobs

Analyze survey and secondary data using statistical and multivariate techniques. Clean, structure, code, and summarize datasets; use SPSS, R, Excel, Python, and VBA to conduct analyses and automate procedures. Prepare Excel outputs and PowerPoint presentations, explain findings to internal and external clients, support analytic planning, and ensure accurate, timely delivery of market research work.

Supports an insurance coverage practice by creating and maintaining claims, policy, spending, invoice, payment, reserve, exposure, and policy-erosion spreadsheets. The role compiles and analyzes monthly reports, prepares client reports, assesses potentially covered expenses, and assists attorneys with claims-related analysis. Success requires advanced Excel skills, strong analytical ability, accuracy, organization, communication, and the ability to manage multiple deadlines and sensitive information.

Validate and maintain customer data used for order processing and fulfillment. Responsibilities include researching and analyzing data, cross-checking systems, resolving discrepancies with stakeholders, supporting reporting, testing business processes, analyzing root causes, and promoting data governance and continuous improvement.

Entry-level MetOcean Data Analyst supporting NOAA NDBC at Stennis Space Center. Analyze meteorological and oceanographic observations, identify observational errors and system anomalies, use scientific programming/analysis tools, and produce clear written and customer-facing documentation while managing varied workloads.

Coordinates district PEIMS and TSDS data collection, auditing, and reporting. Ensures reporting departments and campuses meet strategic goals and Texas Education Agency mandates by supporting accurate, timely data submissions. Provides campus training and ongoing assistance to improve data quality and collection compliance.

Performs accurate data entry, verification, updating, cleanup, organization, and maintenance across databases, spreadsheets, and digital systems. Maintains electronic records, converts documents into digital formats, prepares basic reports, retrieves information, tracks assignments, and supports scanning and filing. The role requires confidentiality, attention to detail, strong organization, dependable independent work, effective time management, and reliable remote-work capabilities.

Build and maintain IBM Planning Analytics TM1 cubes, dimensions, rules, integrations, templates, and reports supporting budgeting, forecasting, and financial reporting. Troubleshoot and optimize existing models, manage changes, document solutions, and perform SIT, regression testing, defect tracking, and go-live support during MECCA’s SAP ERP transformation. The role works closely with Finance, FP&A, and IT.

Own Patlytics’ data warehouse, transformation layer, ingestion pipelines, usage metering, billing data models, BI, and reporting infrastructure. Build production systems in Python and SQL, establish data quality and monitoring standards, and translate ambiguous business questions into reliable analytical models. The role partners closely with the CTO and stakeholders, provides technical direction, and may grow into team leadership. Front-end experience for internal tools and reporting interfaces is a bonus.

Import, transform, and load data from external sources into production databases. Ensure data consistency and quality, collaborate with internal/external stakeholders, support segmentation updates, and assist with displaying data in web applications. Begin onsite for training in Mesa, AZ, then transition to a hybrid schedule.

Own Tomo’s data function, strategy, roadmap, platform health, and cross-functional measurement. Build and lead teams spanning data science, analytics, business intelligence, and data engineering. Design experiments, marketing attribution models, SQL queries, and dbt models while maintaining reliable data infrastructure. Partner with Product, Engineering, and executives to improve instrumentation, translate business questions into insights, and establish scalable data processes.