Top Data & Analytics Jobs in Pensacola, FL

Leads complex analytics initiatives for the Consumer Deposits portfolio, owning Net Present Value methodology and models. Translates ambiguous business questions into scalable datasets, analytical products, and actionable insights supporting pricing, marketing, product, customer, and strategic decisions. Partners with senior stakeholders and cross-functional teams, manages projects independently, improves analytical methodologies and governance, and mentors analysts.

Analyze personal loan product performance, customer behavior, funnel conversion, campaigns, and key performance metrics. Design measurement frameworks, identify tracking gaps, conduct root cause analysis, develop A/B tests, support analytics infrastructure, and create dashboards. Partner with Product, Engineering, Data Science, and Business teams to translate insights into product improvements and increased origination volume.

Lead advanced Amazon and commerce analytics using Amazon Marketing Cloud, advertising, and retail data. Build queries, dashboards, reports, tests, forecasts, and actionable client-ready recommendations. Partner with client, media, operations, data science, and reporting teams to support investment, optimization, and retail decisions. Ensure analytical quality, documentation, automation, and measurement rigor while identifying growth opportunities and communicating technical insights to clients.

The Senior Data Analyst will support Growth and Product teams through paid-channel analysis, user journey optimization, activation and retention insights, and churn reduction. Responsibilities include developing experiments, analyzing ad network performance, improving tracking and instrumentation, ensuring data quality, and enabling self-service analytics. The role requires translating complex data into actionable recommendations and communicating effectively across technical and non-technical teams in a fast-paced SaaS environment.

Owns performance analytics for General Liability claims processes, monitoring trends, diagnosing root causes, evaluating process improvements, and recommending priorities to leadership. Partners cross-functionally with claims, actuarial, underwriting, data, and field operations teams. Presents metrics and action items, validates deployed solutions, supports process optimization, and translates customer and business needs into actionable data-driven solutions.

Designs, develops, and maintains reporting programs for the Vendor Management Organization. Extracts and analyzes data from multiple sources, performs trend and root-cause analysis, identifies process improvements, and presents KPIs and insights to stakeholders using Power Query, Python, and Tableau.

Design, build, and optimize enterprise ETL/ELT pipelines, cloud data lakes, lakehouse solutions, data models, and semantic layers across AWS and Azure. Ensure data quality, security, performance, reliability, and governance while supporting analytics, reporting, and AI initiatives. Collaborate with product, engineering, IT, and business stakeholders to translate requirements into scalable data solutions, monitor pipeline health, troubleshoot issues, and improve data architecture and practices.

Lead full-cycle customer data migrations from legacy DME/HME systems into a SaaS platform. Responsibilities include mapping, extracting, cleansing, transforming, and validating patient, inventory, claims, order, and payer data; resolving discrepancies; collaborating with implementation and product teams; supporting customers; and documenting migration processes.

The Business Intelligence Engineer manages enterprise data sources, supports data quality and governance, and translates transactional data into KPIs for business teams. Responsibilities include guiding analyst teams, handling data inquiries, supporting advanced analytics, monitoring data performance, defining data requirements with stakeholders, and presenting findings to technical and non-technical audiences. The role requires SQL, Tableau, relational database, and data modeling or analytics expertise.

Own Step’s analytics data models, transformation pipelines, metric definitions, semantic layer, and data quality practices. Partner with Product, Marketing, Engineering, Risk, Growth, and Finance to establish trustworthy data foundations and enable self-service analysis. Build reusable query, visualization, and code components; apply AI-augmented workflows; and collaborate with data scientists, analysts, and platform engineers. The role requires strong SQL, modeling, pipeline, BI, communication, and independent ownership skills, with occasional travel for team onsites.