Top Hybrid Data & Analytics Jobs

Build and maintain scalable data models, pipelines, ETL workflows, and streaming solutions using dbt, PySpark, AWS Glue, Databricks, and Kafka. Apply software engineering practices including testing, version control, code reviews, and integration. Partner with data scientists, stakeholders, and data stewards to improve data quality, governance, accessibility, and reliability. Translate requirements into development plans and Jira stories while collaborating on data strategy, tooling, integrations, and deployments.

Conduct quantitative research for algorithmic trading and market-making strategies. Design, test, deploy, and monitor models supporting pricing, risk, and execution. Build research and backtesting tools, analyze large datasets and model outputs, and collaborate with traders, developers, and senior researchers to translate hypotheses into production trading decisions. Continuously improve strategies based on market dynamics, data drift, and performance results.

Lead the rebuild and modernization of the company’s data platform by designing and implementing an Azure and Databricks lakehouse. Responsibilities include managing Azure Data Factory pipelines, developing Delta Lake architectures, creating data models and transformations with SQL and Python, establishing development standards, monitoring data quality, troubleshooting performance issues, leading technical projects, and mentoring or hiring data engineering staff.

Develop data models, analyses, dashboards, and automated reporting for a collectibles marketplace. Partner with engineering, product, and data platform teams to define business metrics, identify customer experience and growth opportunities, inform strategy, and lead initiatives that improve business performance.

Lead consumer lending analytics and strategy across credit card, HELOC, and Auto/RV products. Evaluate portfolio performance, member behavior, pricing, targeting, risk, profitability, and growth opportunities. Develop strategic recommendations, forecasts, business cases, and analytical frameworks using SQL and large datasets. Partner with finance, credit risk, marketing, operations, and technology leaders while managing, coaching, and developing an analytics team.

The Senior Data Scientist will lead analytics projects, define success metrics, run experiments, and present data-driven insights to influence product and business strategies.

Leads enterprise data architecture initiatives by translating business requirements into scalable data designs, assessing impacts across systems and pipelines, modernizing legacy environments, and guiding AI/ML data solutions. Partners with data leaders and stakeholders, manages project plans, monitors implementation progress, and communicates risks and mitigation plans. Requires expertise in cloud and hybrid environments, data platforms, data engineering or architecture, and team or project management.

Lead enterprise data architecture across warehouses, data lakes, downstream applications, and AI workloads. Design data models, Medallion Architecture, ELT standards, semantic layers, APIs, access controls, and reusable datasets. Partner with business and BI teams to drive adoption, conduct architecture reviews, establish platform standards, and mentor data and analytics engineers. The role requires deep Snowflake, dbt, data modeling, pipeline, and semantic-layer expertise, with experience in AI data patterns and cloud infrastructure preferred.

Leads service delivery analytics and planning for Trust & Safety Operations. Owns KPI, SLA, productivity, quality, and operational reporting; builds dashboards, forecasting models, workforce plans, and monitoring frameworks. Manages the analytics and planning team, supports vendor budgeting and cost models, identifies operational risks and process improvements, and translates data into recommendations for leadership and service delivery teams.

Owns go-to-market funnel reporting and analytics from lead generation through pipeline, bookings, and ARR. The role analyzes conversion, pipeline quality, CAC, LTV, marketing ROI, and sales efficiency across segments and channels. It builds dashboards, forecasts, and executive insights while partnering with Marketing, Sales, Revenue Operations, and Finance on attribution, data integrity, budgeting, and investment decisions.