Top Data Analyst Jobs in NYC, NY

Lead data strategy and analytics for Compliance, focusing on Financial Crimes/AML. Drive ETL, source validation, and analyses to quantify compliance risk, support regulatory engagements, and influence product and compliance roadmaps. Communicate insights through visualization and cross-functional collaboration while leading multiple high-impact workstreams.

Support asset-level data intake, validation, and exception triage for private markets. Ensure data quality and standardization, perform trend analysis to inform product and process improvements, coordinate onshore/offshore delivery, document operational logic, and collaborate with Product, Data, and Engineering teams to improve workflows and client reporting.

The Data Analyst will analyze complex datasets to provide insights, design experiments, optimize reporting solutions, and support cross-functional teams.

As a Senior Product Data Analyst, you will analyze payment data, define metrics, design experiments, and provide insights to enhance product decisions.

As a Lead Data Analyst, you'll optimize transaction costs and success rates in payments, analyze failure patterns, and collaborate with Data Science and Engineering to enhance payment strategies and model performance.

Design and build data solutions using open-source technologies; enable self-service data frameworks; partner with business to create metrics, dashboards, and actionable analysis; apply statistical and ML methods for insights; enforce data quality, metadata, lineage, and governance; collaborate with tech teams on security and data access; build tools to monitor and report data quality.

Support Finance and Sales by reconciling and validating revenue KPIs (ACV, ARR, renewals), building reporting datasets and dashboards, collaborating with CRM and data engineering teams, and participating in month-end and quarter-end close activities. Provide ad-hoc analysis and stakeholder-facing explanations of metric derivation and data quality.

Partner with workforce and business leaders to analyze headcount, role mix, capacity, cost, and OKRs; build analytical models, present clear data storytelling to senior stakeholders, and improve data quality and reporting to support workforce planning and strategic decisions.

Support multimodal neuroscience research by programming data handling and analysis pipelines, implementing QA/curation protocols, testing processing/feature generation, maintaining databases, assisting data collection (MRI/EEG/physiology/behavior), contributing to literature reviews and manuscripts, and presenting at team meetings.

Own and maintain the Propel PLM system to ensure accurate product data, BOMs, and integrations with ERP/CRM. Configure workflows, roles, and data structures; validate product and manufacturing documentation; support cross-functional teams; lead PLM projects; drive data quality improvements, user training, and continuous system enhancements to align PLM with production and supply chain operations.

As a Principal Data Analyst, you will drive workforce analytics initiatives, develop self-service data tools, and ensure data quality, leveraging various programming and data visualization tools.

The Lead Data Analyst will analyze user journeys, recommend product changes, develop dashboards, and drive strategic initiatives through data analysis and experimentation frameworks.

Lead data analytics for network and merchant compliance: design tests, build models and reporting, mine large datasets, improve data quality and processes, advise stakeholders on risk, and manage a team to implement compliance analytics solutions.

The Data Analyst in Revenue Operations will analyze sales data, build models, and create reports using SQL, Excel, and BI tools to empower business decisions.

The Senior Data Analyst will enhance marketing measurement programs, conduct in-depth analyses, develop data models, and mentor peers, focusing on actionable insights.

Support FP&A by owning forecasting and reporting in Pigment, building dashboards and KPIs, maintaining data infrastructure, partnering cross-functionally to improve datasets and models, and delivering strategic analysis for senior management.

The Senior Revenue Operations Analyst will ensure data quality across systems, integrate third-party data into CRM, define quality metrics, and collaborate cross-functionally to improve data processes.

Lead analytics for Brigit's Line of Credit product: define core metrics, forecast revenue and costs, design experiments, optimize underwriting and collections, evaluate and monitor predictive models, build dashboards, and deliver ad-hoc analyses to influence product, finance, and leadership decisions.

The Senior Data Analyst for Payments will analyze and optimize payment processes, collaborate with cross-functional teams, and ensure regulatory compliance to improve financial operations.

The Senior Data Analyst will own data models and drive analytical initiatives, ensuring data quality and reliability while providing insights to stakeholders. Responsibilities include managing dashboards and supporting the team with analytics.

Analyze data center operational practices, identify risks and inconsistencies, and create scalable enablement materials (SOPs, runbooks, templates, training). Partner with technicians, engineers, security, and compliance to validate procedures, support audit readiness, and leverage modern tooling including AI/LLM workflows to accelerate documentation and onboarding.

The Forward Deployed Data Analyst will optimize SQL queries, troubleshoot analytics pipelines, and collaborate with customers to enhance their data systems. Responsibilities include diagnosing issues, proposing structural improvements, and coordinating with engineering teams.

Own and validate quantitative financial datasets used for factor and risk models: onboard and reconcile vendor sources, build automated data validation/monitoring, investigate anomalies, write Python/SQL pipelines, maintain documentation and runbooks, and collaborate with internal teams and vendors to productionize and maintain reliable data workflows.

Lead requirements gathering, document functional specifications, review data models, validate implementations, support production deployments, investigate data quality and collaborate with technology teams to ensure data completeness and lineage.

Collect, consolidate, and validate data from disparate sources; document functional specifications; review data models; gather business requirements; support deployments; investigate data quality and bridge gaps with technology teams.