Top Data & Analytics Jobs

Analyze product and underwriting data to identify trends, develop metrics, conduct ad hoc queries, and support operational dashboards. Translate quantitative findings into actionable recommendations for leadership through data storytelling. The role requires collaboration with business teams, strong analytical problem-solving, and proficiency with Excel, PowerPoint, Python, or SQL.

Build and maintain data pipelines using ETL/ELT, Databricks, Python, Spark, and Airflow. Manage data transformations, databases, schemas, and data quality controls. Support reporting, analytics, and machine learning integrations while deploying solutions in AWS. Automate workflows, troubleshoot data issues, document processes, and collaborate with data scientists, analysts, database administrators, and business stakeholders.

Supports business analytics projects by translating business questions into data requirements, developing relational data models and Power BI reports, validating data integrity, analyzing ad hoc datasets, documenting projects, training users, and presenting findings. The intern also contributes to digital tool development and continuous improvement initiatives.

Lead solution onboarding and customized client delivery for alternative investment services. Translate business requirements into technical specifications, configure systems, manage reporting and file-extract projects, develop end-to-end test cases, oversee process improvements, analyze client requests, and coordinate with developers, QA, fund accountants, investor services, relationship managers, and external stakeholders.

Designs, builds, and operates scalable batch and streaming data pipelines, ELT/ETL workflows, and cloud data infrastructure. Ensures data reliability, quality, observability, lineage, performance, and cost efficiency across enterprise domains. Partners with analytics engineers, BI teams, and business stakeholders to deliver analytics-ready datasets. Establishes engineering practices including version control, CI/CD, automated testing, infrastructure-as-code, architecture standards, and documentation.

Analyze frontline operations performance across specialists, queues, vendors, and sites. Identify root causes behind SLA, AHT, accuracy, and utilization changes; quantify impacts; monitor emerging issues; and provide actionable recommendations to operations leadership. Define requirements for specialist metrics and dashboards, collaborate with analytics and support teams, explain findings in business reviews, and use AI analytics tools to accelerate investigations.

Architects, builds, and scales data pipelines, ETL processes, data models, and platforms using dbt, Snowflake, Airflow, and Fivetran. Leads data engineering best practices including CI/CD, data quality, governance, and observability. Collaborates with Data Science, Engineering, Product, and business stakeholders; mentors engineers and data scientists; contributes to architecture and technical decisions; and participates in on-call support.

Design and maintain ETL workflows, data models, and pipelines supporting business intelligence and reporting. Work with Redshift, NoSQL, AWS, and data integration tools to ensure reliable, governed data. Build dashboards in Omni, Mode, and Mixpanel, analyze finance and accounting data, and partner with Finance, Product, Operations, and Engineering to translate business needs into actionable insights and promote data-driven decision-making.

Analyzes operational data across repossession, total loss, insurance, and recovery functions to identify trends and improvement opportunities. Develops and maintains Tableau and Excel dashboards, monitors KPIs, prepares performance reports, documents processes, supports special projects, and recommends actions to improve efficiency, service levels, and compliance. Collaborates with business leaders on requirements, process enhancements, and operational issue resolution.

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.