Top Entry Level Data & Analytics Jobs

Develop and deploy Python data pipelines and business data objects for applications. Responsibilities include translating design requirements into implementations, building CI/CD pipelines, implementing source-to-target mappings, resolving complex technical issues, and conducting unit and integration testing. The role also promotes software quality practices, supports new program development, and collaborates cross-functionally on complex systems work.

Analyze and resolve pricing control exceptions involving fees, interest, refunds, and servicing activities. Review processes, build automated analytical workflows, develop reporting, and apply anomaly detection and responsible generative AI concepts. Partner with analysts, managers, product, operations, analytics, and technology teams to improve monitoring, documentation, and controls. Communicate findings clearly, manage multiple workstreams, and drive continuous improvement across card pricing operations.

Own the full lifecycle of Finance visualization and conversational analytics products, from data modeling and metric validation through deployment and adoption. Build executive dashboards and AI-enabled natural-language experiences using Tableau, ThoughtSpot, and Databricks. Partner with Finance, Product, Data Engineering, and platform teams on data quality, governance, security, compliance, and performance. Manage analytics roadmaps and backlogs, establish delivery standards, mentor analysts, and communicate insights to senior Finance leaders.

Lead financial reporting delivery and data pipeline operations while improving data controls, quality, lineage, audit readiness, and regulatory compliance. Automate manual workflows using Alteryx, Python, and SAS; resolve production issues; partner with Technology on data models, testing, and enhancements; and analyze datasets for stakeholder reporting. Contribute to metadata, data dictionaries, responsible AI, and scalable data modernization initiatives.

Designs and implements scalable data, analytics, and AI solutions for clients. Builds and optimizes data models and ETL/ELT pipelines, performs testing and validation, translates business requirements into technical solutions, documents architectures, manages workstreams, supports client training, and contributes to governance, risk tracking, and solution design.

Develops automation, data pipelines, databases, analytics, machine learning models, reporting tools, and web applications for package technology quality assurance. Analyzes manufacturing, product, and reliability data to support risk assessment and root-cause investigations. Collaborates with engineering teams, communicates findings, trains users, maintains software and reports, and shares analytics best practices across global teams.

Supports marketing analytics and insights through dashboard development, recurring and ad hoc reporting, data hygiene, SQL/SAS/Hadoop analysis, and customer feedback programs. The role manages Voice of the Customer and Customer Lifetime Value reporting, coordinates research-partner deliverables, monitors survey data, triages stakeholder requests, maintains knowledge documentation, and develops actionable recommendations for Marketing and eCommerce stakeholders.

Participate in a rotational analyst development program focused on using data and technology to analyze performance trends, identify business opportunities, and support insurance operations. Complete training, mentorship, and rotational assignments while developing strategic, technical, analytical, communication, and business skills. Use tools such as Excel, SQL, Python, R, SAS, Snowflake, Power BI, and emerging AI technologies to generate business insights.

Analyze marketplace and product performance using SQL, support experiments and advertising-effectiveness studies, build reusable datasets, QA metrics, and document analytics. Collaborate with analytics engineers, data scientists, product managers, engineers, and commercial teams while developing skills in statistics, causal inference, Python/R, and marketing science.

Build and maintain Databricks data pipelines using SparkSQL and PySpark; ingest, transform, model, and validate data from payer, provider, claims, platform, and API sources. Support automated file exchanges, dashboards, testing, data governance, access controls, and audit logging. Use AI coding agents responsibly, participate in design and code reviews, document technical decisions, and collaborate with business teams. The role is based in Miami and targets candidates graduating between December 2026 and June 2027.

Designs and maintains data visualization, reporting, and business intelligence platforms. Builds Python frameworks, dashboards, reporting applications, and analytics solutions for traders, quants, and developers. Optimizes data access and query performance, supports Looker, BigQuery, Plotly Dash, and Jupyter Notebooks, translates stakeholder requirements into technical solutions, and leads migrations across tools, libraries, and data sources.

Conduct quantitative research to design, implement, and deploy mathematical trading models. Build custom tools for exploratory analysis, apply rigorous quantitative techniques to solve complex problems, and collaborate with traders and developers to productionize models while maintaining best practices.