Top Data & Analytics Jobs

Leads requirements gathering, future-state process design, and scope definition for global product master data workstreams. Improves process efficiency, data quality, governance, and scalability through cross-functional stakeholder collaboration. Supports workflow definition, user stories, acceptance criteria, backlog readiness, prioritization, rollout activities, change management, and delivery handoffs. Uses SQL and enterprise data integration knowledge to validate and analyze structured data while supporting ERP-related processes and regional expansion.

Principal technical leader responsible for architecting and operating relational data marts, dbt data products, semantic layers, and shared data services. Builds scalable solutions in Databricks, Delta Lake, and Spark; owns data products through testing, deployment, documentation, monitoring, and support. Establishes governed metrics, data quality, lineage, observability, and access controls while partnering cross-functionally to resolve ambiguity. Mentors engineers, improves reusable frameworks, and supports BI, data science, and AI use cases.

Lead the architecture and development of scalable data pipelines and infrastructure. Clean, standardize, and model construction data; build tools for data quality and performance; design integration flows with product, engineering, and customers; apply Python, SQL, and ETL practices; and use AI/ML to improve data classification and mapping. Establish data governance and reliability standards in a high-ownership startup environment.

Analyzes affiliate ecommerce performance, translates data into actionable recommendations, develops dashboards and reporting, identifies growth opportunities, designs tests, and supports editorial, business development, and social teams. The role also maintains analytics processes, automates workflows, and leads ad hoc analysis during major commerce periods.

Own Hazel’s end-to-end data platform, including ingestion, transformation, orchestration, reliability, and warehouse performance. Build low-latency, reliable pipelines and clean data models, ship integrations for e-commerce and advertising sources, and automate integration and transformation work using AI agents. The role requires production ownership of large-scale data infrastructure, strong Python, SQL, and dbt expertise, orchestrator experience, and effective documentation.

Leads commercial data infrastructure, business intelligence, reporting automation, AI-enabled analytics, data governance, and process improvement. Builds dashboards and analytical solutions for forecasting, pricing, profitability, customer segmentation, and market intelligence. Partners cross-functionally with sales, marketing, operations, supply chain, finance, and IT to improve data quality, streamline workflows, and replace manual processes. The role is fully on-site in Louisville, Kentucky, with travel of up to 20%.

Performs complex data analyses, develops reports, dashboards, models, and statistical solutions, and communicates findings to technical and nontechnical audiences. The role produces written summaries and presentations, supports quality improvement, manages multiple projects, represents analytics leadership with clients, and serves as a technology expert and resource for junior staff. Responsibilities include improving analytics efficiency and delivering accurate outputs on schedule.

Manage and develop a team of 4–6 analysts delivering customer reporting and analytics. Lead complex analyses, establish standards for rigor and reproducibility, oversee project delivery, present findings to stakeholders, and partner with Product and Engineering on measurement products and AI-enabled analytics tools. The role requires deep advertising measurement expertise, client-facing communication, dashboard development, and hands-on proficiency in SQL, Python, Excel, statistical methods, and analytics platforms.

Design and operate production-grade data pipelines, backend services, APIs, and agentic AI workflows. Build scalable systems that ingest, transform, and operationalize data for customer-facing and internal applications. Translate ambiguous operational workflows into reliable architectures, improve service observability and performance, and own technical decisions across data modeling, system design, security, and compliance.

Build data models, dashboards, reports, and visualizations using SQL, Power BI, Python, DAX, and Power Query. Analyze, clean, validate, and model data; troubleshoot systems; define alerts; identify process bottlenecks and automation opportunities; and improve data collection and reporting. Collaborate with business stakeholders, document projects, maintain data integrity, communicate insights, and train nontechnical teams on data usage.