Top Data & Analytics Jobs

Own and evolve business-critical data products, designing scalable data services, APIs, models, and batch or streaming pipelines. Lead projects through implementation, deployment, monitoring, and production support while applying AI tools throughout the software development lifecycle. Collaborate across engineering, product, data, and business teams, contribute to architecture and governance, improve reliability, and mentor earlier-career engineers.

Lead design, implementation, and optimization of enterprise data architecture and pipelines for manufacturing AI initiatives. Develop data models, ETL processes, data warehouses/lakes, APIs, and repeatable pipelines across multi/hybrid-cloud environments. Guide data standards, feature engineering for data scientists, and leverage AI, automation, and CI/CD for scalable solutions.

Own CX data infrastructure, including pipelines, data models, governance, monitoring systems, and Streamlit dashboards. Apply advanced statistics and AI-assisted anomaly detection to identify customer experience opportunities and performance issues. Partner with CX leaders, data science, engineering, and Salesforce teams to define metrics, answer causal questions, and guide operational decisions. Establish data quality standards, validate AI-generated outputs, communicate insights clearly, and mentor data practitioners.

Lead the architecture, development, and productionization of scalable data pipelines and governed data products in Databricks. Transform heterogeneous source data for analytics, AI, and machine learning; establish data quality, observability, governance, and delivery standards; and support experimentation, model development, deployment, and monitoring. Lead cross-team initiatives, influence technical roadmaps, mentor engineers and data scientists, and develop reusable AI and multi-agent data capabilities.

Designs and maintains data collection infrastructure, resilient pipelines, ETL/ELT processes, data warehouses, streaming architecture, BI reporting, and analytical systems. Troubleshoots technical defects, performs root-cause analysis, maintains specialized datasets, and owns end-to-end data collection, extraction, and cleansing. The role requires experience with AWS, Google Cloud, ETL tools, programming languages, NoSQL databases, and BI platforms.

Build and maintain production data pipelines, Airflow DAGs, Snowflake infrastructure, and Data Vault 2.0 models. Ensure data quality, observability, security, governance, reliability, and cost efficiency through automated testing and CI/CD. Lead technical projects end-to-end, collaborate with stakeholders, and support SOX-relevant reporting while developing expertise in commercial insurance data and processes.

Designs and maintains scalable data pipelines, data quality frameworks, and cloud-native data solutions using Spark, Databricks, Hadoop, AWS, and advanced SQL. Implements validation, monitoring, observability, and automated remediation capabilities. Applies AI/ML, anomaly detection, and statistical modeling to improve data quality. Provides production support, resolves complex incidents, contributes to architecture and engineering standards, and collaborates with cross-functional data and engineering teams.

Owns the full lifecycle of business intelligence solutions, including requirements gathering, dashboard and report development, data modeling, visualization, governance, and production delivery. Partners with business, laboratory, engineering, and product stakeholders to perform root-cause analyses, develop datasets, support self-service analytics, and guide system changes. Requires advanced SQL, BI tool expertise, stakeholder management, and experience with agentic workflows.

Build, optimize, and maintain scalable data pipelines and reusable datasets for business intelligence, marketing, and data science. Develop federated data validation frameworks, optimize queries and data models, and protect user privacy and security. The role uses Python, SQL, AWS, Airflow, PostgreSQL, Iceberg, Snowflake, Spark, and other modern data-stack technologies. It is based in New York City with in-office work three days per week.

Maintain and extend Snowflake and dbt marketing data models, dashboards, semantic models, KPI frameworks, and AI-agent validation processes. Investigate data quality issues, document metric definitions, assess downstream impacts of marketing technology changes, and translate stakeholder reporting needs into durable self-service data structures. The role requires collaboration across marketing, sales, IT, and finance to ensure trusted data and accurate AI-generated insights.