Top Data & Analytics Jobs in NYC, NY

The Complex Data Exchange Specialist manages data integration processes, ensuring high-quality data exchange between clients and systems, while mentoring peers and driving process improvements.

Build and maintain data transformation logic, data models, quality checks, documentation, reports, and dashboards using SQL and analytics engineering practices. Collaborate with engineering, analytics, and business stakeholders to translate requirements into reliable, actionable data that supports enterprise intelligence and AI capabilities.

Designs and delivers scalable analytics engineering solutions using dbt, SQL, Python, Databricks, and BigQuery. Responsibilities include dimensional modeling, transformation pipelines, data products, testing, monitoring, governance, CI/CD, semantic-layer development, and performance optimization. The role owns data products end to end, collaborates with stakeholders and data teams, improves engineering standards, and mentors junior engineers. Preferred experience includes AI-assisted pipeline development, observability, data catalogs, lineage, graph models, and financial services.

Designs and owns scalable analytics engineering solutions, including dbt transformation pipelines, dimensional models, data products, testing, governance, deployment, and monitoring across Databricks, PostgreSQL, and BigQuery. Develops advanced SQL and Python transformations, optimizes performance, improves CI/CD and SDLC practices, builds semantic-layer metrics, and contributes to AI-enabled pipeline automation. Collaborates with stakeholders, data scientists, ML engineers, data stewards, and integration teams while mentoring junior engineers.

Leads product analytics and partners with product and executive leadership to guide strategy, roadmap prioritization, experimentation, and optimization. Translates customer and business questions into measurable outcomes, analytical roadmaps, segmentation, funnel and cohort analyses, and evidence-based recommendations. Owns product measurement frameworks, dashboards, data quality, instrumentation, and governance while communicating insights, trade-offs, and actions to senior stakeholders.

Summer interns support business analytics and digital transformation initiatives by building dashboards, analyzing datasets, developing visualizations, researching digital product enhancements, and assisting with automation and process improvement. The role involves translating data into actionable insights, collaborating with professionals, and using technology to support business decisions. Candidates must be enrolled in an accredited four-year institution and graduate between December 2027 and May 2030.

Design and execute enterprise data architecture strategy, including logical, physical, operational, and analytical data models. Establish data management, governance, quality, privacy, and security standards while supporting scalable cloud data platforms. Collaborate with product owners, architects, engineers, data scientists, and business teams to translate requirements into data solutions. Lead modernization, legacy simplification, performance optimization, integration, streaming, and organizational change initiatives across insurance and financial services data environments.

Lead development and operationalization of enterprise data risk frameworks, KRIs/KPIs, and risk registers. Produce dashboards and reporting, track remediation, partner with data, technology, risk, and compliance teams, and support data protection, governance, and adoption activities across cloud data environments.

Lead a small second-line team providing independent oversight of enterprise data governance. Assess governance frameworks, data ownership, quality controls, risk identification, and regulatory alignment. Deliver actionable insights to senior management, partner with first-line teams to improve governance maturity, and validate controls and issue management practices across the firm.

Design, configure, and operate data governance tooling for unstructured and structured data. Implement metadata cataloging, lineage harvesting, automated data quality checks, and governance workflows. Build prototypes, dashboards, and connectors using Python, SQL, Databricks, and Streamlit. Collaborate with stakeholders to define critical data elements, prioritize remediation, produce documentation, and drive tool adoption via trainings and enablement.