Top Data & Analytics Jobs

Own Hometap’s product and marketing event-data ecosystem, including taxonomy, tracking plans, governance, Segment Protocols, schema validation, and source management. Partner with marketing, product, and engineering to define instrumentation and event specifications. Build monitoring for schema conformance, event volume, and property completeness, while documenting standards and managing event review and deprecation processes.

Supports private-market asset data workflows by processing and reconciling data, monitoring exceptions, maintaining quality standards, analyzing trends, documenting operational logic, coordinating offshore teams, and partnering with Product, Data, and Engineering on process and product improvements.

Designs, develops, and operates scalable batch and streaming data pipelines and products. Integrates data from multiple sources, improves reliability and performance, establishes reusable engineering patterns, and supports AI, search, analytics, and machine learning workloads. Partners with technical and business stakeholders on architecture and requirements, while providing technical guidance and mentoring to less-experienced engineers.

Owns data systems workstreams for public fixed income investment operations. Leads process improvement and automation, develops reports, dashboards, and data models, manages data quality and issue resolution, and performs portfolio analysis, forecasting, benchmarking, and financial modeling. Partners with investment, client, and business teams on reporting and analytics while supporting technology implementations, translating business needs into technical requirements, and mentoring junior analysts.

Staff Data Engineer responsible for setting technical direction, architecting scalable data platforms, leading cross-functional initiatives, improving reliability and performance, and enabling AI-assisted analytics and development. The role includes hands-on development with Python, SQL, cloud data warehouses, orchestration, infrastructure as code, Kubernetes, Kafka, and streaming pipelines, along with mentoring, technical advocacy, documentation, code reviews, and on-call troubleshooting.

Lead business intelligence initiatives supporting Snapchat’s Sales organization. Analyze data from multiple sources, identify campaign and industry trends, build dashboards and reports, deliver actionable revenue insights, and recommend optimization strategies. Partner with cross-functional teams on measurement strategies, product feedback, revenue programs, and growth initiatives. Communicate complex analyses to technical and non-technical audiences and influence business decisions through compelling data stories.

Develop and monitor fraud-detection models and pattern-recognition algorithms for lending products. Analyze large datasets using Python and SQL, identify emerging fraud trends, and collaborate with Fraud Operations to refine features and reduce false positives and negatives. Conduct ad hoc analyses, recommend risk and verification strategies, communicate findings to cross-functional partners, and support improvements to underwriting and verification processes.

Build, optimize, and maintain scalable data pipelines and reusable datasets for business intelligence, marketing, and data science. Develop a federated data validation framework, optimize queries and data models, and protect sensitive healthcare data through privacy and security best practices. The role uses Python, SQL, AWS, Airflow, PostgreSQL, Snowflake, and modern data-stack technologies. This position is based in New York City and requires working onsite three days per week.

The AI Analyst Co-op will use Python, HTML, SQL, APIs, and AI tools to generate scalable insights for go-to-market teams. Responsibilities include personalizing BDR outreach by buyer persona, distributing market insights across sales, marketing, and partner resources, and collaborating with Platform Advisory. The role requires strong analytical skills, attention to detail, problem-solving persistence, and an understanding of technology’s role in improving operational efficiency.

Leads WHOOP’s data governance function, defining strategy, operating models, ownership, data contracts, event taxonomy, cataloging, access policies, quality standards, and privacy compliance. Builds and coaches a senior team while partnering across engineering, analytics, product, legal, GRC, and InfoSec. Establishes governance frameworks, automated quality monitoring, access controls, compliance practices, and metrics, while embedding standards into technical workflows and advancing AI-powered governance.