Top Analytics Engineer Jobs in NYC, NY

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.

Leads data and analytics engineering for Zero Trust maturity assessment and implementation measurement. Designs common data models, integrates and validates enterprise data, develops maturity and risk metrics, supports prioritization and gap analysis, and establishes data quality, lineage, traceability, and documentation practices. Partners with dashboard and automation specialists to streamline reporting and data refresh workflows.

Build and maintain data infrastructure, integrate veterinary partner systems, develop dbt models and pipelines, provide analytics modeling, testing, reporting, and deliver actionable insights to customers and internal teams.

Design and build scalable data foundations, pipelines, semantic models, and AI-grounded tooling. Lead platform-level AE tooling, reusable dbt patterns, observability, cost strategies, and agentic AI workflows. Partner with stakeholders, guide team technical development, and drive cross-functional data initiatives from ambiguous problems to production.

Design and implement end-to-end data solutions on Microsoft Fabric and Azure for insurance datasets. Build ingestion pipelines (batch, streaming, API), Lakehouse architectures, and data models. Enforce governance, security, compliance, and cost/performance optimizations while guiding engineers and stakeholders on architectural best practices.

Design and implement end-to-end Microsoft Fabric data solutions (ingestion, Lakehouse, warehouse, real-time). Create architectures, reusable frameworks, and governance patterns for insurance datasets. Lead ingestion pipelines, optimize performance/cost, integrate with Azure services and security, support regulatory compliance, and provide technical direction to engineering and analytics teams.

Design and maintain a dbt-based business data layer for GTM metrics, centralize and reconcile multi-source data, implement automated data quality and reconciliation checks, align and document metrics with stakeholders, mentor analytics team, and lead root-cause investigations into complex data anomalies.

Design, build, and maintain forecasting and analytical models, ETL/ELT pipelines, dashboards, and production-grade model deployments. Analyze operational and financial data, develop KPIs, support executive strategic inquiries, and partner cross-functionally to translate data into actionable insights while embedding AI to scale workflows.

Develop and deliver healthcare analytics solutions using Ursa Studio, reusable data models, data marts, and measures. Partner with clients and cross-functional teams to scope engagements, provide consulting and training, maintain documentation and security standards, and contribute scalable analytics components to Ursa’s SaaS and AI-assisted platforms. The role requires strong healthcare data expertise, SQL proficiency, client-facing communication, and knowledge of healthcare regulations and standards.

Build and maintain reliable data warehouse models and pipelines to power GTM metrics, dashboards, and automation. Partner with Sales, CS, Product, and Engineering on instrumentation, CRM and usage modeling, data quality, observability, forecasting, and actionable data products for GTM teams.

Design and implement ad measurement metrics, perform deep-dive analyses, build reporting and automated pipelines, create visualizations, and provide actionable insights to improve advertiser measurement and platform health.

Design and maintain scalable dbt data models and ELT pipelines in Snowflake. Architect data foundations to power AI and LLM-enabled workflows, operationalize core SaaS metrics, and build reusable data assets for analytics and activation. Partner cross-functionally to embed analytics into workflows and ensure scalable, secure architecture.

Design and maintain scalable dbt data models and ELT pipelines in Snowflake; architect reusable data assets, governance, and SaaS metrics; build AI-enabled data foundations and LLM workflows; integrate analytics into operational tools and partner cross-functionally to productionize AI-driven use cases.

Build and maintain data pipelines, metrics, and dashboards to quantify consumer product security risk. Perform hands-on analytics into device and authentication signals, shape risk measurement strategy, partner with product and security teams, and influence leadership reporting. Drive a unified, scalable data foundation that enables cross-team alignment and proactive risk detection.

Design and maintain data pipelines, transformations, and analytics-ready data models for the RADAR platform. Build and maintain BI dashboards and self-serve reporting, write scalable SQL and Python/R code, implement testing and data validation, automate recurring workflows, and partner with Product, Engineering, Sales, and Marketing to define KPIs and translate business requirements into reliable data products.

Build and maintain dbt models and SQL transformations to create analytics-ready datasets, implement tests and documentation, and govern metric definitions. Design Power BI reports and dashboards, improve self-service analytics, maintain CI/CD for dbt deployments, and collaborate with data engineering and business stakeholders to ensure data quality, lineage, and scalable architecture.

Design and build GTM data infrastructure in BigQuery, ingesting and modeling Salesforce and other GTM sources. Create durable dbt models and pipelines, ensure data quality with testing and monitoring, partner with sales leadership to translate requests into trusted tables, and produce dashboards and analyses for forecasting, pipeline, segmentation, comp, and territory reporting.

Bridge enterprise customers' legacy BI to Hightouch by reverse-engineering complex marketing reports, building reusable reporting frameworks, engineering and documenting semantic/context layers, ensuring accurate SQL generation, and training non-technical marketers while collaborating cross-functionally to deliver reliable, idempotent AI-driven analytics.

Build and maintain auditable data pipelines in Snowflake and dbt to ensure financial reconciliation and audit readiness. Partner with Accounting, Operations, Engineering, and Reporting to investigate data issues, improve integrations via APIs, and own claim reimbursement and contract sales data workflows.

Lead analytics engineering efforts to design and build dbt models, maintain a semantic/metrics layer, ensure Snowflake performance, establish modeling/testing/documentation standards, partner with Data Science/Product/Engineering, and deliver native warehouse data products to customers.

Build and scale the analytics foundation: own dbt projects and Snowflake warehouse, create trusted datasets and semantic layers for AI, build ELT pipelines from APIs, implement testing/observability and CI/CD, design reconciliation models, enable stakeholder self-service, and partner cross-functionally to drive data-driven decisions across GTM, Product, Finance, People, and Operations.

Design, build, and optimize modular dbt pipelines and data models; translate ambiguous product requirements into production-grade data assets; diagnose query performance and lower warehouse compute costs (including migration to Snowflake); enforce code hygiene, data security, testing, and peer review; mentor junior engineers and adopt AI-assisted development tools to speed test generation and legacy discovery.

Own and evolve the analytics engineering platform: design and maintain certified dbt models, enforce modeling/testing/documentation standards, build semantic and metadata layers, optimize Snowflake performance, partner with analysts and data engineering to migrate business logic and ensure reliable, AI-ready analytics, and mentor teammates on analytics engineering best practices.

Hands-on engineering role to design, build, and maintain scalable ETL/ELT data pipelines, data warehouses and lakes, and data models. Optimize batch and near-real-time processing, implement query optimization and data quality checks, support AI data workflows, and collaborate with architects, analysts, and stakeholders.

Design, build, and maintain scalable, documented data models and the Gold data layer using dbt/Coalesce. Ensure data quality through automated tests, observability, version control, and CI/CD. Optimize SQL and warehouse performance, collaborate with Data Engineering and Analytics, and support semantic layers and BI catalogs to enable trusted, self-service insights for Product, Finance, and GTM teams.