Top Entry Level Data & Analytics Jobs

Own Qualitate’s data engineering foundation by building pipelines for structured and unstructured data, designing relational and non-relational data models, and developing AI-powered enrichment workflows. Establish data quality monitoring, integrations, schemas, contracts, lineage, and documentation. Partner with Applied AI, Research, and Engineering while shaping the company’s early data architecture and enabling customer data integrations.

Develops and maintains Power BI dashboards and data models, defines KPIs with stakeholders, validates data accuracy, and collaborates with Data Engineering on optimized analytical models. The role documents data lineage and reporting assumptions, supports self-service BI through communications and training, and automates manual reporting processes. It requires SQL, Power BI, DAX, Power Query, data modeling, and data warehouse knowledge, plus strong communication and cross-functional collaboration skills.

Serves as a data-management subject matter expert supporting intelligence and counterintelligence organizations. Establishes data frameworks, governance policies, metadata standards, quality controls, interoperability processes, KPIs, dashboards, and analytical products. Advises leadership on modernization, resource allocation, and operational planning; coordinates with data scientists, engineers, security personnel, and Intelligence Community stakeholders. Requires active TS/SCI CI Poly and on-site work in a DIA SCIF.

Own daily data operations, including data collection, pipeline monitoring, quality validation, root-cause investigation, SQL-based cleaning, and Metabase dashboards. Partner with engineering and deployment teams to resolve data issues, standardize metrics, document processes, and build tooling that improves reliability and scalability for government clients.

Own subscription marketing and lifecycle analytics, including acquisition, retention, churn, CRM performance, attribution, engagement, cohort analysis, dashboards, and data quality. Write SQL queries, evaluate campaigns and offers, identify retention drivers, and translate findings into recommendations for marketing and cross-functional stakeholders.

Produces regular, ad hoc, and specialized internal and client reports; manages reporting schedules and distribution lists; investigates data inconsistencies; gathers stakeholder requirements; communicates findings; and compiles data for business analysis. The role requires Excel and Windows proficiency, troubleshooting ability, strong communication and customer service skills, prioritization, and independent work. Previous reporting experience and ProLaw knowledge are preferred.

Extracts and analyzes business data using statistical analysis, data mining, modeling, dashboards, and routine reporting. Maintains analytical models, documents audit trails, identifies relationships and underlying issues, and provides fact-based recommendations and solutions. Uses Microsoft Office, Power BI, and Adobe Acrobat Pro to support process development and capture business operations information.

Build and scale data insights capabilities, write SQL queries in Snowflake, transform data with Python, and create dashboards in Qlik Sense and Tableau. Support data governance, preprocessing pipelines, predictive modeling, documentation, version control, and automated data workflows. Collaborate with Data Engineering teams to deliver real-time insights for robotics and supply chain automation projects.

Analyze and improve structured and unstructured financial data pipelines using Python, SQL, statistical methods, machine learning, and LLMs. Responsibilities include data cleansing, validation, anomaly detection, reconciliation, dataset onboarding, API integration, model evaluation, workflow monitoring, documentation, and root-cause investigation. The role also involves explaining technical findings, managing assigned datasets, and supporting reliable production data processes in a quantitative trading environment.

Develop and maintain enterprise manufacturing dashboards, KPI scorecards, and executive reports across multiple plants. The role standardizes metrics, analyzes operational performance, consolidates ERP data, validates reporting accuracy, resolves data quality issues, and automates reporting. It partners with plant leadership, Operations, Finance, IT, and Data Stewards to identify trends, benchmark performance, and support data-driven decisions.