Top Analytics Engineer Jobs in NYC, NY

The Data Analyst will enhance remittance operations by analyzing transaction data, improving disbursement processes, and designing performance metrics dashboards while ensuring user satisfaction and operational efficiency.

The Disbursement Analyst will optimize transaction processes by analyzing data, maintaining performance dashboards, driving defect reduction, and communicating insights to stakeholders, focusing on improving user experiences in remittances.

Build and maintain Salesforce reports and Sigma dashboards, manage CRM data hygiene and Data Cloud integrations from the Databricks data lake, handle ad-hoc commercial reporting, and support AI initiatives tied to sales and commercial strategy.

Design, develop, and optimize high-performance, cloud-native analytics platforms and data pipelines. Build monitoring, alerting, dashboards, and integrate quantitative models into trading systems. Collaborate with researchers, data scientists, and stakeholders, apply secure coding and unit testing, and produce documentation while leading or mentoring teams to deliver scalable analytics solutions.

Design, implement, test, and deploy high-performance C++ APIs exposing a mortgage analytics quantitative library for interest rate modeling, prepayment/default analysis, derivative valuation, hedging, and forecasting. Improve performance, reliability, and usability of the library and collaborate with business, model development, validation, and IT teams.

The Senior Data Analytics Engineer analyzes and delivers business intelligence solutions, builds data pipelines, and collaborates with clients to meet their data-driven needs.

Design, implement, and maintain dbt/Snowflake ELT transformations to convert raw clinical and multimodal health data into trusted, analysis-ready models; enforce data quality, lineage, testing, CI/CD, and documentation; collaborate with clinical and ML teams to harmonize ontologies and support de-identification and ETL across health systems.

The Senior Analytics Engineer at Rockstar Games will develop data infrastructures, optimize data models, and collaborate with Data Scientists and Engineers to provide actionable insights.

As a Billing & Analytics Software Engineer, you'll design and maintain the billing pipeline, integrate billing systems, and develop customer-facing analytics dashboards to support revenue recognition and operational observability.

Lead architecture, build, and operate customer-facing analytics platform: design ELT and metric governance, implement low-latency serving (OLAP), ensure reliability/performance, mentor engineers, partner with product, and use AI coding tools to accelerate development.

Designs, builds, and operates ELT data pipelines and analytical datasets for Platform Analytics. Curates and models source data into conformed datasets, ensures performance, reliability, and cost efficiency, contributes to target-state architecture, mentors junior engineers, and uses AI coding tools to accelerate development and delivery.

Own and evolve the analytics platform: build and maintain dbt data models, govern semantic metrics in Lightdash, ensure data quality and observability, and partner with analysts and stakeholders to enable reliable self-service reporting.

The Senior Analytics Engineer will design data models, build data pipelines, and support analytics needs across teams, enhancing data infrastructure.

Build and maintain a cloud-native, high-SLA Alpha Platform for pre-trade analytics, risk, hedging, and portfolio optimization. Work across full stack from data ingestion and processing to AI-driven analytics and client APIs. Drive system design, own features end-to-end, mentor peers, and promote engineering standards, observability, and CI/CD best practices.

Design, build, and maintain dbt-based analytics-ready data models and curated data marts powering a PBM financial risk engine. Onboard and optimize claims, pricing, contract, and utilization data, implement data quality controls, support forecasting and risk metrics, and enable advanced analytics and ML operationalization for finance and underwriting stakeholders.

As an Analytics Engineer, you will develop data pipelines, analytics frameworks, and dashboards to support data-driven decisions and enhance customer acquisition. Collaborate with various teams to transform business needs into scalable data solutions while ensuring data integrity and quality.

Lead end-to-end analytics engineering: build and maintain BigQuery data warehouse, dbt models, CDC and streaming pipelines (Materialize), orchestration, observability, and BI/analytics products to enable self-service insights and real-time data products.

Design, build, and maintain scalable dimensional data models, semantic layer, and production transformation pipelines. Collaborate with Data Engineering, Data Science, BI, DevOps, and Product to enable AI-ready data, ensure data quality and governance, support ML feature delivery, and coach analytics team members for robust, production-ready data products.

The Staff Product Analytics Engineer will enhance product data usage across teams by owning data infrastructure, creating tools for self-service data, and ensuring data-driven decisions. Responsibilities include collaborating with various teams to improve data insights and maintain data pipelines.

The Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

The Analytics Engineer I designs and maintains data models and transformations, collaborating with stakeholders to ensure data quality and support analytics needs in a healthcare environment.

Build and maintain data pipelines and dbt models, own reporting and dashboards across Finance, GTM, Product, and Engineering, support third-party financial data integrations, enable GTM and customer-facing analytics, and ensure data quality, testing, and documentation.

Lead design and implementation of shared, reusable analytics data models and pipelines. Drive standardization, governance, observability, and CI/CD for analytics; partner with Data Science, Infrastructure, and Product to certify metrics, modernize orchestration, and integrate AI-native tooling.

Own and improve the dbt modeling and semantic layers, implement CI/CD, tests, observability, and governance; build governed metrics for BI and AI consumption; partner with Data Engineering and Risk to stabilize pipelines and improve data quality and documentation.

Own and build reliable analytics data products: define metrics, create and maintain dbt models, design dashboards for growth/finance/ops, improve data quality via testing and monitoring, and enable self-serve analytics for stakeholders.