Top Entry Level Data & Analytics Jobs

Maintains and validates financial and securities reference data for clients, ensuring accuracy, completeness, and compliance. Responsibilities include monitoring investment data, evaluating vendors, documenting workflows, supporting data solutions, analyzing accounting and custody information flows, performing basic SQL tasks, and collaborating with clients, data providers, and internal teams. The role requires developing knowledge of fixed income, equities, corporate bonds, derivatives, and related investment processes.

This co-op develops analytical solutions for fulfillment performance reporting. Responsibilities include understanding fulfillment processes, consulting on data pipelines, developing data models and dashboards, analyzing trends, and providing insights and recommendations. The role collaborates with product managers, visualization analysts, data scientists, IT, technical teams, and fulfillment operations.

Monitor and verify live sports data feeds during games, identify and resolve discrepancies in real time, cross-check multiple data sources, and escalate data issues. Maintain knowledge of game schedules, rosters, rule changes, and performance metrics to ensure accurate data interpretation. The role requires strong sports knowledge, exceptional attention to detail, focus during live events, and familiarity with spreadsheets, data tools, APIs, and real-time tracking systems.

Migrates customer data from legacy and competitor systems into SaaS products using SQL-based tools. Performs extraction, transformation, cleanup, validation, auditing, data mapping, and quality assurance. Develops repeatable scripts and workflows, collaborates with customers and cross-functional teams, supports testing and defect resolution, and documents migration processes. The role increasingly uses BigQuery, cloud platforms, automation, and intelligent tools while supporting global delivery across regions and time zones.

Early Career Analyst supporting Affirm’s Risk & Analytics organization through data analysis, portfolio monitoring, reporting, risk and growth strategy insights, and cross-functional collaboration. The role uses SQL and Python to build scalable reports, support machine learning and risk model deployment, assess lending regulations, and improve product launches and market expansion. Analysts will integrate AI tools into workflows while validating outputs and developing expertise in consumer lending and quantitative business decision-making.

Develop market intelligence, forecasting, statistical models, scenario analyses, and automated reporting for supply chain decisions. Integrate internal and external datasets using Python, R, SQL, and other tools; build dashboards; support data governance; and translate analytical findings into recommendations for procurement, planning, and executive stakeholders.

Build and maintain scalable data pipelines, analytical data models, backend data systems, business rules, metrics, and data sources. Partner with teams across finance, growth, product, customer support, sales, and marketing to support analytical needs and improve data-driven decision-making. Own data engineering problems end to end and help advance organizational data practices.

Build and operate governed Finance data infrastructure on Databricks, including scalable pipelines, integrations, semantic layers, data-quality controls, observability, and AI-enabled services. Partner with analytics and enterprise teams on architecture, data contracts, security, recoverability, and production ownership. Optimize platform costs through FinOps practices and use AI coding assistants while validating performance, correctness, and security.

Collects, processes, vets, and analyzes geospatial and statistical data using Esri tools, Power BI, Python, or R. Supports research projects, client meetings, reports, presentations, dashboards, technical assistance, and proposal development. Works on focused project tasks under supervision while communicating findings clearly and maintaining project information and spending records. The role is hybrid in Houston and includes travel to client sites.

Build and maintain Databricks data pipelines using SparkSQL and PySpark; ingest, transform, model, and validate healthcare data from payer, provider, platform, and API sources. Support automated file exchanges, dashboards, downstream workflows, testing, governance, access controls, and audit logging. Participate in design and code reviews, use AI coding tools responsibly, and progressively own data engineering work while handling protected health information.