Top Data & Analytics Jobs

Designs and operates scalable batch and streaming data pipelines and AI-ready data products across Databricks, Snowflake, Kubernetes, and cloud services. Develops governed data assets, semantic models, search and retrieval capabilities, and data quality processes. Supports Snowflake Cortex and Databricks initiatives, production troubleshooting, orchestration, performance optimization, CI/CD, and observability. Partners with business and technical teams on roadmaps and adoption, evaluates AI-assisted engineering tools, mentors engineers, and establishes reusable platform patterns.

Build and operate production-grade ELT pipelines from Benchling, Salesforce, and third-party systems into Snowflake using dbt. Own ingestion, transformation, warehouse, BI, governance, monitoring, testing, access controls, privacy, cost, and performance. Partner with AI engineering to provide trusted data for internal AI applications and agentic workflows, contribute to warehouse and metrics-layer architecture, and translate cross-functional stakeholder needs into scalable data solutions.

Creates and manages data connections, validates and transforms incoming portfolio data, and normalizes external datasets into Clearwater’s standardized format. Automates monitoring and reporting, integrates APIs and FTP connections, and improves auto-reconciliation through data analysis and process enhancements. Collaborates with Operations and Development, troubleshoots data-feed issues, documents workflows, communicates technical concepts to nontechnical stakeholders, and provides light on-call support.

Lead a global analytics team to transform financial and operational data into actionable insights. Partner with stakeholders to align analytics with strategy, build scalable Power BI dashboards integrated with Snowflake, apply predictive modeling and ML, enforce data governance, drive automation with Python and Power Automate, and improve data architecture and reporting to inform decision-making and business performance.

Lead the architecture and development of Vercel’s Finance Data Platform supporting revenue recognition, consumption-based billing, forecasting, reporting, and compliance. Build reliable batch and real-time pipelines using Kafka, ClickHouse, Tinybird, and Snowflake; establish governance, lineage, auditability, monitoring, and fault tolerance; translate Finance and Accounting requirements into scalable systems; guide architecture and roadmap decisions; collaborate with Security, Legal, Compliance, Audit, FP&A, and RevOps; and mentor data engineers.

Leads a data engineering team responsible for data modeling, batch and streaming ETL pipelines, analytics-ready datasets, data governance, and data quality. Hires, coaches, and manages engineers while setting technical direction and partnering with engineering, product, and business stakeholders. Oversees scalable data foundations across regulatory, customer, business, content, and growth domains, and advances practical AI automation and AI-ready infrastructure.

Build and maintain data pipelines integrating laboratory instruments, automation systems, operational platforms, APIs, robotics, databases, and files. Develop SQL transformations, data models, validation checks, orchestration, monitoring, and alerting to support analytics, dashboards, troubleshooting, and AI systems. Document infrastructure and datasets for regulatory compliance while collaborating with engineering, laboratory operations, data science, and automation teams. This is an on-site role with on-call and occasional weekend or holiday support.

As the first in-house Data Engineer, you will own the data infrastructure, build and maintain data pipelines, and collaborate with teams to ensure quality data access and insights.

Build and maintain analytics data models, warehouse tables, views, and automated pipelines using dbt and Snowflake. Ensure data quality, consolidate business-unit data, develop ThoughtSpot dashboards, and analyze complex datasets to support product and operational decisions. Partner with product, engineering, and operations stakeholders, define metrics, improve self-service analytics, leverage AI tools for automation, and document data processes.

Own the end-to-end commerce data supply chain, including third-party ingestion, schema-drift detection, entity resolution, deduplication, freshness SLAs, and retirement of stale records. Design and operate an LLM validation fleet with evaluation harnesses, golden sets, escalation paths, and defensible unit economics. Build production data systems at scale, establish machine-checkable quality metrics, and work independently with the founder in a fast-moving, in-person environment.