Top Analytics Engineer Jobs in NYC, NY

Develops business intelligence, reporting, data pipelines, dashboards, and geospatial solutions for complex real estate portfolios. Transforms and integrates data from multiple sources, builds scalable analytics products, automates reporting processes, and supports portfolio planning, occupancy strategy, workplace initiatives, and operational decision-making. Partners with stakeholders and clients to translate business needs into technical solutions, communicate insights, train users, and improve analytics adoption. The role also supports geospatial capabilities and leverages emerging AI-enabled tools.

Build and steward Netflix advertising quality-of-experience data, from device telemetry and instrumentation through data pipelines and dashboards. Collaborate with product engineering, data engineering, and data science teams to measure user-perceived performance, improve data quality, document user flows, and establish monitoring and measurement best practices. The role requires strong SQL and Python skills, technical communication, stakeholder management, and comfort working independently in an ambiguous environment.

Build and maintain dbt models and daily pipelines, monitor and triage ETL runs, develop Omni BI views, collaborate cross-functionally, and document data assets to enable data-driven decisions.

Build Sailor Health’s foundational data platform from scratch. Responsibilities include developing Snowflake and dbt pipelines integrating EHR, CRM, and communications data; creating data-powered web applications; automating clinical and billing workflows with Python; analyzing therapy transcripts and patient data using AI and machine learning; tracking clinical outcomes; and designing scalable data architectures in collaboration with clinicians, engineers, founders, and other teams.

Build and maintain production dbt data models, marketplace reporting, data quality checks, and self-service analytics. Investigate anomalies, define KPIs, measure product and user-journey changes, and support experimentation. Apply LLMs to analytics workflows by connecting models to warehouse data, engineering context, and evaluating output reliability. Partner with business, product, yield, and account teams while contributing to future offer optimization and quantitative modeling.

Build and maintain Clair’s ELT pipelines and dbt data models, ingest new source systems, and create trusted datasets for analytics and business decisions. Improve data quality, testing, documentation, observability, governance, and access controls. Partner with analysts, data scientists, engineers, and business stakeholders to define metrics and resolve data issues. Support feature pipelines, optimize Snowflake costs, and occasionally provide ad hoc analysis.

Own the analytics engineering roadmap, architecture, and production data models. Build and optimize dbt models using SQL, Python, and Databricks; establish dimensional models, governed metrics, testing, documentation, lineage, monitoring, and CI/CD practices. Partner with data engineering, analysts, BI developers, and stakeholders to deliver reliable analytics products, improve performance and cost efficiency, investigate data quality issues, and mentor team members.

Build and operate high-throughput data pipelines, analytics engines, analytical databases, and AI platforms for quantitative trading teams. Develop streaming and batch systems, trading metrics, time-series models, monitoring, RAG pipelines, prompt-management tools, and production AI agents. The role requires strong Python and Java engineering, AWS and Kubernetes expertise, advanced SQL, financial data experience, and a focus on reliability, scalability, data integrity, and performance.

Own January’s trusted analytics layer by designing a dbt-driven semantic layer in Snowflake, standardizing metrics and data contracts, improving client reporting, expanding data capture, and managing analytics costs. The role enables trustworthy self-service across dashboards, chatbots, and future AI agents while partnering with Data Engineering, Analytics, Borrower Support, and Client Acquisition.

Build and maintain scalable dbt-based data models and analytics-ready datasets for pharmacy benefit management and care navigation. Translate healthcare workflows into auditable data products, implement data quality testing and reconciliation, optimize warehouse performance, and establish documentation, lineage, governance, CI/CD, and observability practices. Collaborate with technical and business teams to support reporting, financial analysis, self-service BI, and future AI-enabled analytics capabilities.

Own production-grade analytics data products, including reusable dbt models, dimensional models, metrics, access controls, tests, documentation, and Lightdash guidance. Partner with stakeholders across GTM, Finance, and data teams to define requirements, reconcile conflicting source data, validate results, and publish trusted metrics. Diagnose data quality issues, collaborate with data engineering, review modeling changes, and establish shared standards. Regularly use and evaluate AI coding agents in analytics workflows.

Own product analytics and growth measurement by partnering with product and design, defining metrics and event tracking, building dbt models and data pipelines, creating dashboards, and analyzing activation, engagement, retention, funnels, cohorts, and experiments. Establish trustworthy product data standards, identify product-market-fit indicators, surface insights, and influence roadmap decisions while collaborating with clinical and research analytics teams.

Design and own scalable dbt data models and semantic layers, define and govern company-wide metrics, build data quality and observability practices, enable analytics for experimentation and AI workflows, and align cross-functional stakeholders while mentoring analytics engineers.

Lead analytics engineering by developing scalable data models, defining analytics requirements, and delivering data products for company stakeholders. Establish standards for data structure, quality, documentation, testing, version control, and development practices. Partner with analysts, engineers, product managers, and other teams to improve data accessibility and decision-making. Requires expertise in SQL, Snowflake, dbt, BI tools, and analytics engineering practices, plus fintech experience.

The Staff Analytics Engineer leads the design and implementation of data solutions, enhancing analytics capabilities across Zocdoc, collaborating with multiple teams to drive strategic insights.

Build and maintain governed Silver/Gold data models and semantic assets on Databricks. Translate stakeholder KPIs into testable, reusable transformations, apply enterprise modeling standards, enable domain self-service, ensure data sensitivity and governance, and review domain-contributed models for production readiness.

Develop and modernize Tableau dashboards for People Analytics, improving visualization, usability, performance, and self-service insights. Design and maintain Tableau row-level security for sensitive employee data, including dynamic access controls and entitlement mappings. Build optimized data sources and models using Snowflake and SQL, troubleshoot data and integration issues, document solutions, and collaborate with technical and business stakeholders on scalable analytics products.

Build and maintain reusable analytical data products, canonical entities, data marts, semantic layers, and governed metrics across business domains. Establish modeling, documentation, quality, lineage, and change-management standards; improve data reliability, self-service analytics, and production governance. Partner with Data Platform, Trust, Data Science, Applied AI, and business stakeholders on orchestration, observability, access controls, feature tables, model outputs, and shared metric definitions. Remain hands-on with SQL, Python, testing, code reviews, documentation, and technical coaching.

Architect and maintain NetBrain’s first cloud data warehouse, ELT pipelines, transformation layer, semantic models, and BI foundation. Own data quality, governance, lineage, access controls, and curated models supporting product usage, customer adoption, account health, and go-to-market reporting. Partner with stakeholders to establish reliable metrics and a standardized source of truth, while supporting AI-assisted analytics and natural-language reporting use cases.

Build and maintain data models, pipelines, metrics, dashboards, and analytics supporting identity graph construction, entity matching, and identity resolution. Partner with engineering and data science teams to productionize matching logic, evaluate match quality, monitor data quality and drift, and manage identity-provider relationships. Translate complex identity concepts into actionable insights for technical and non-technical stakeholders while helping define privacy-conscious product and data priorities.

Own the company’s data function by designing and optimizing Snowflake architecture, pipelines, integrations, data models, BI dashboards, metric definitions, governance, and data-quality standards. Partner with leadership and cross-functional teams to translate operational needs into trusted data products. Automate recurring reporting, troubleshoot ingestion issues, perform ad hoc analysis, and establish scalable analytics practices as the first dedicated analytics hire.

Lead design and delivery of scalable analytics engineering solutions: build dbt transformation pipelines on Databricks/Postgres/BigQuery, develop tested dimensional models and feature tables, implement data quality and governance, improve SDLC and CI/CD, create reusable dbt patterns and semantic metrics, and engage stakeholders to translate requirements and mentor junior engineers.

Implements and supports security data ingestion pipelines across applications, cloud services, infrastructure, security tools, and business systems. Develops transformations, parsing, normalization, enrichment, masking, validation, and delivery workflows. Troubleshoots data quality and pipeline issues, documents architectures and data flows, coordinates with client stakeholders, and supports SIEM, detection, investigation, observability, and automation use cases. Requires data engineering experience, scripting, query languages, integration knowledge, and strong client communication.

Build and maintain data ingestion, warehousing, modeling, historical repository, data quality, reconciliation, lineage, and regulatory reporting solutions for financial services risk programs. Analyze complex data issues, support testing and production activities, and collaborate with risk, compliance, audit, technology, and reporting stakeholders. Develop datasets for Power BI and ensure audit-ready documentation in regulated environments.

Build and maintain production-grade dbt data models and pipelines across ingestion, transformation, and visualization. Develop reliable datasets supporting reporting, analysis, finance, revenue recognition, and company decision-making. Collaborate with Finance, Engineering, Sales, Product, and Marketing to understand data needs, improve manual workflows, validate data accuracy, and create scalable solutions using modern data tools.