Top Hybrid Data & Analytics Jobs

Manage Finance-focused data products, collaborating with teams to define requirements and drive delivery for financial analysis and reporting.

Lead product analytics for new financial product initiatives: build business models, segment customers, create datamarts and dashboards, ensure data quality, design experiments, and surface insights using AI-enabled tooling to guide product decisions.

Design, build, and own the full data platform (ingestion, modeling, CI/CD, orchestration, observability, and BI). Implement streaming and batch pipelines for IoT data, enable governed self-service, enforce data governance/privacy/security, evolve AI access patterns, and provide technical leadership and mentorship to data engineers and data scientists to deliver production-ready analytics and ML capabilities.

Lead IEX's Market Data, Access & Member Services team, focusing on driving revenue growth, strategic market data initiatives, and enhancing client services.

The Business Analyst will analyze business data, document requirements, provide insights, and support cross-functional teams. They will also guide junior members and adapt to fast changes.

The Business Analytics Associate analyzes marketing channel performance and client retention, presenting actionable insights to enhance company goals and KPIs.

Design, build, and maintain scalable ETL/ELT pipelines and data warehouse architecture. Partner with cross-functional teams to define KPIs, ensure data quality and governance, own billing data infrastructure, and deliver dashboards and analytics for leadership while mentoring peers.

Design, build, and maintain large-scale data pipelines and DAGs (Airflow) that process imagery, LiDAR, and other sensor data. Improve platform reliability, observability, resource tuning, and Kubernetes deployments. Collaborate with ML, hardware, and full-stack teams; own pipeline design, implementation, testing, and operations in a hybrid NYC role.

Design, build, and deliver scalable data pipelines and products end-to-end. Partner with analytics, product, and engineering to ingest diverse data sources, implement dbt transformations, ensure data quality, CI/CD, observability, monitoring, and cost/performance optimization. Leverage AI/automation for self-service tooling and intelligent pipelines while driving stakeholder alignment and reliable production operations.

Design, build, and own scalable ETL/ELT pipelines, data models, and data infrastructure. Improve data quality, observability, performance, and CI/CD for data workflows. Partner with analytics, ML, software, and product teams, mentor engineers, and resolve complex data issues.