Top Data & Analytics Jobs

Leads the design, development, and ownership of scalable data systems, ELT pipelines, data models, and Snowflake architectures supporting analytics, experimentation, machine learning, and business decisions. Partners cross-functionally to deliver reliable, governed data solutions; improves quality, observability, testing, documentation, and operational practices; mentors engineers; and shapes technical direction through automation, technology evaluation, and AI-enabled engineering.

Build and own trusted data products, dimensional models, semantic layers, and dbt transformations that support analytics, experimentation, machine learning, reporting, and decision-making. Partner with cross-functional teams to translate business needs into scalable datasets, improve data quality and governance, establish engineering standards, and drive self-service analytics. Mentor engineers and analysts while leveraging AI tools to improve development, documentation, and quality.

Design, build, and operate WHOOP’s data platform infrastructure, including lakehouse, warehouse, and streaming systems. Improve performance, reliability, cost efficiency, observability, and developer experience across AWS-based data systems. Develop internal tools, APIs, automation, CI/CD, and infrastructure-as-code practices. Partner with data, ML, analytics, and software engineering teams to support scalable workflows, governance, and production excellence while using AI-assisted development tools effectively.

Design, build, and maintain scalable data pipelines and ingestion frameworks across cloud platforms. Ensure data quality, lineage, storage, and consumption methods. Implement solutions on Kubernetes, Databricks, and Teradata, mentor junior engineers, collaborate with cross-functional teams, and contribute to internal tooling to support analytics and customer-facing data products.

Leads the design and management of data engineering solutions, including pipelines, storage systems, data architecture, quality frameworks, APIs, and data extracts. Deploys solutions across Kubernetes, Teradata, AWS, and Databricks; selects storage technologies; maps data lineage; establishes QA standards; and collaborates cross-functionally to improve data quality, accessibility, and operational efficiency. Requires independent judgment and flexibility for nights, weekends, and variable schedules.

Lead the design and development of scalable, fault-tolerant streaming data platforms, distributed data pipelines, analytics data stores, and cloud-based solutions. Lead engineers, establish data architecture and pipeline best practices, develop and review high-performance code, and collaborate across Agile, product, security, and engineering teams. Mentor developers, support machine learning and microservices initiatives, and apply emerging technologies to deliver reliable data solutions.

Develop and optimize validation KPI dashboards, Jira workflows, and Google Sheets tools. Use Python, Power BI, Databricks, and automation to analyze validation data, identify risks, and improve reporting. Create validation processes, documentation, training materials, and standard work while supporting Lean Six Sigma continuous improvement and change management. Maintain data repositories and help validation teams track readiness, accountability, and completion milestones.

Develop and deploy Python data pipelines and business data objects. Design software solutions, implement source-to-target mappings, build CI/CD pipelines, resolve complex technical issues, and perform unit and integration testing. The role also promotes software quality, engineering best practices, team improvement, and development of digital data capabilities.

Designs and operates batch, streaming, and real-time data platforms and pipelines using Spark, Kafka, Iceberg, Airflow, and cloud infrastructure. Owns data lake evolution, data quality, observability, reliability, governance, event instrumentation, schema management, and privacy controls for PII, retention, deletion, and residency. Leads complex initiatives end-to-end, troubleshoots production issues, mentors engineers, and develops AI agent harnesses that enforce data engineering standards and quality gates.

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.

Design and build analytics-ready data models and a governed semantic layer in Databricks/Snowflake. Translate stakeholder requirements into reusable datasets, implement data quality controls, optimize model performance, document lineage and metrics, and enable self-service BI.

Design, build, and maintain scalable data systems and pipelines (batch and streaming). Implement data formats, modeling, and infrastructure; support deployment automation, stakeholder requirements, and data governance to enable analytics and reporting.

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.

Design, build, and maintain a Microsoft Fabric Lakehouse and Azure Synapse data warehouse. Develop and optimize ETL/ELT pipelines, dimensional/semantic models, and data quality checks. Tune SQL and Spark workloads, implement monitoring/alerting, handle production incidents, and integrate source systems and APIs. Partner with stakeholders, follow Agile practices, document runbooks, and participate in code reviews.

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.

Lead data engineering initiatives by designing scalable data pipelines, services, architectures, models, and structures using Hadoop, Ab Initio, Exadata, SQL, and cloud platforms. Analyze and troubleshoot complex data in warehouse environments, support risk management and business intelligence, resolve production and data integrity issues, gather requirements, lead designs, and mentor engineering staff throughout the project lifecycle.

Leads enterprise data platform architecture, engineering standards, automation, security, governance, observability, and cloud integration. Designs scalable batch and streaming pipelines connecting SAP, legacy systems, Databricks, and enterprise data products. Guides SAP transformation data harmonization, analytics and reporting strategy, infrastructure as code, CI/CD, reliability, and platform roadmaps. Provides hands-on technical leadership, mentors senior engineers, collaborates across business and transformation teams, and supports critical incidents.

Design, develop, and maintain scalable batch and streaming data pipelines using Python, PySpark, SQL, Databricks, Kafka, and Azure Data Factory. Provide production support, incident resolution, vulnerability remediation, platform upgrades, and reliability improvements. Optimize data models and processing workloads, implement automation and AI-enabled solutions, support CI/CD and cloud modernization, and ensure healthcare data security, governance, and privacy compliance.

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.

Creates and manages data connections, validates and transforms client files, integrates external APIs and FTP feeds, and normalizes portfolio data into Clearwater’s standardized format. Monitors and automates daily processes, improves auto-reconciliation, resolves data issues, and implements operational enhancements. Communicates technical workflows to stakeholders, documents procedures, and provides light on-call support.

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.

Designs and delivers large-scale, governed data pipelines and infrastructure across the full data lifecycle. Responsibilities include coding in Python and SQL, optimizing Airflow, dbt, AWS, Kafka, and Iceberg/Parquet workflows, implementing data quality and observability, contributing to architecture and tool evaluations, mentoring engineers, collaborating with stakeholders, and participating in on-call support.

The Senior Data Platform Engineer will design, maintain, and enhance Hinge's data platform, including data pipelines, monitoring systems, and user feedback incorporation while ensuring data quality and system reliability.

Designs, builds, and supports scalable data warehouse solutions, including pipelines, transformations, models, source-to-target mappings, DDL documentation, and SCD models. The role supports data governance, metadata management, migration, modernization, troubleshooting, and root cause analysis while collaborating with engineering, quality, business analysis, and reporting teams. It also leads data requirements analysis, data preparation, data infrastructure design, and scalable implementation across multiple platforms.

Design, build, and maintain large-scale ETL pipelines and data warehouses (Redshift) with strong data modeling, governance, and quality controls. Optimize systems for performance and reliability, integrate and deploy AI/ML models, troubleshoot and support Redshift, collaborate with data scientists and stakeholders, and produce technical documentation and knowledge-base articles.