Top Hybrid Data & Analytics Jobs

Provide accurate valuations and risk assessments for single-family homes using internal tools and pricing model outputs. Incorporate local market knowledge, collaborate with sales, homes, data science and engineering to improve pricing operations and ensure competitive, consistent offers.

Validate and review CO APCD-derived data products using SQL and visualization tools, perform output comparisons and project-level analyses, follow quality control standards, document findings, and communicate results to technical and non-technical stakeholders while supporting continuous improvement of data QA processes.

Lead commercial analytics to diagnose sell-through, pricing, promotions, assortment, and retailer execution. Build causal and incrementality measurement, design test-and-learn, support elasticity and margin analyses, and deliver executive-ready insights and frameworks to optimize revenue, margin, and promotional efficiency across channels and retailers.

Own the analytics foundation for Fieldguide Insights: design the semantic layer, define canonical tables/metrics, migrate report logic into governed dbt models, harden pipelines with monitoring and alerts, ensure data quality, and partner across product, engineering, and customers to enable reliable BI and emerging LLM/MCP analytics.

The EO Imagery Analyst will provide geospatial and imagery analysis to improve data operations for ML algorithm testing, working with EO imagery products and metadata.

Support Business Banking with analytics, reporting, dashboards, and automation to drive deposit acquisition, onboarding, funded balance growth, and retention. Produce forecasting and pipeline reporting, provide deposit strategy and profitability insights, enable go-to-market and campaign analysis, and standardize onboarding and data processes to improve efficiency and data quality.

Embed with Revenue leadership to identify high-value questions, build scalable analyses, models, and visualizations, and use AI agents and LLM tooling to accelerate insight delivery. Advise data teams on models, metrics, and semantic layers; validate AI-assisted outputs; communicate recommendations to technical and executive audiences; and develop reusable tooling and documentation.

Lead Philo's Data team, overseeing data science, engineering, and analytics to provide reliable insights and data-driven solutions across the company.

Lead the Data Platform Engineering team owning ETL/reverse-ETL pipelines, replication, and production datastores. Define architecture, roadmap, standards (IaC, observability, schema contracts), mentor engineers, drive incident response and durable fixes, and partner on cross-team technical decisions and operational reliability for data correctness, completeness, and timeliness.

Build and productize externally consumable data assets from concept to delivery. Define schema, governance, SLAs, documentation, and delivery (Snowflake share, SFTP, API). Partner cross-functionally to productionalize datasets, monitor quality, iterate on customer feedback, and create repeatable productization frameworks.