Top Analytics Engineer Jobs in NYC, NY

As a Data Analytics Engineer, you'll build and manage data models in Snowflake, structure datasets for optimal AI performance, and own data transformation while ensuring quality and accessibility for all teams.

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.

Lead technical design and architecture for analytics engineering: build and govern the semantic/metrics layer, optimize dbt and Snowflake for performance and AI use cases, mentor engineers, partner with Data Science and Engineering, drive data quality, CI/CD, observability, and deployment of third-party data and emerging tooling to support analytics, ML, and agent-based products.

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.

Design, build, and maintain end-to-end data models and pipelines to support CX operations, compliance reporting, and customer insights. Create dashboards and metrics, enforce data quality and governance, automate recurring workflows, and partner with stakeholders and broader data teams to deliver self-serve analytics solutions.

Design, build, and maintain scalable dbt transformation pipelines and analytical data products on cloud platforms. Own data product lifecycle from source alignment and modeling to deployment, testing, monitoring, and governance. Implement automated data quality, observability, and metadata practices; develop reusable macros and semantic-layer assets. Collaborate with stakeholders, mentor peers, and promote analytics engineering standards to support reporting, advanced analytics, and AI use cases.

Design, build, and maintain data transformations and models using SQL and dbt to support enterprise reporting and AI initiatives. Implement data quality controls, create analytics datasets, and collaborate with engineering and business teams to deliver trusted dashboards, metrics, and information products.

Build and maintain production-grade data models and pipelines for the Compliance Data Mart, implement data quality frameworks and monitoring, partner with engineering to resolve data gaps, support regulatory exams and audits, and automate manual workflows into scalable pipelines and self-serve tooling.

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.

Design, build, and maintain high-quality data models and a governed semantic/metrics layer (Snowflake semantic views) to enable self-service analytics. Partner with analysts and business stakeholders to define metrics, produce certified BI content (Tableau, Redash), ensure data quality and privacy, optimize model performance, and mentor junior team members.

Join a 12-month Orbit program to consult with customers, prototype AI/ML and LLM-based solutions, build Celonis data models with SQL and PQL, execute proofs-of-concept, present value to stakeholders, and collaborate with Product & Engineering to drive value realization.

Design and maintain scalable data models and end-to-end data marts, build and optimize ETL pipelines (dbt, Fivetran), ensure data quality and observability, collaborate with stakeholders to define KPIs, and use version control and whiteboarding tools to document and iterate on data products.

The role focuses on implementing cost optimization solutions for cloud infrastructure through automation and collaboration across teams, enhancing FinOps practices.

Build, operate, and extend data platform and application-layer services enabling secure, self-service data applications at scale. Implement provisioning, CI/CD, access control, integrations, observability, automation, and incident response while contributing to semantic layer and AI-ready tooling roadmaps.

The Analytics Engineer will design and maintain data infrastructure for business intelligence, optimize analytics processes, and ensure data quality and governance.

Lead design and delivery of company-wide data models and analytics products. Gather requirements, define outcomes, enforce data standards, and partner with analysts, engineers, and PMs to scale reliable data solutions and practices.

The Analytics Engineer will build and optimize data systems, analyze catalog performance, and collaborate across teams to enhance decision-making processes.

Lead architecture and implementation of a scalable data lakehouse and streaming platform. Define technical strategy, build resilient systems, optimize performance, mentor engineers, and partner across teams to enable analytics and ML use cases using modern data platform technologies.

As a Senior Analytics Engineer, you'll design data systems, implement data warehouses, optimize pipelines, ensure data quality, and mentor stakeholders on data best practices.

Design, build, and maintain trading and servicing applications and workflows for Fixed Income and Lending using Python, Java, Spring Boot and frontend frameworks. Integrate internal/external data, implement straight-through processing, leverage GenAI to automate operations, perform testing, monitor production, and develop CI/CD pipelines while working in an agile scrum team.

Owner of the analytics foundation: design and maintain BigQuery/dbt models, pipelines, and Looker Studio reporting; build AI workflows and web apps on GCP; integrate marketing, e-commerce, and fulfillment data; ensure GCP ops, security, and reliability; use AI-assisted tools daily and collaborate with business and client stakeholders.

The Senior Analytics Engineer will design and implement scalable data solutions for Finance Analytics, reconcile data accuracy, and collaborate across teams to enhance financial datasets and reporting workflows.

Owner of disbursement efficiency: analyze payment rails and routing KPIs, investigate transaction failures, build dashboards and dbt models, partner with Disbursements, Operations, and Product to improve success rates, cost, speed, and user experience.

Design, build, and maintain scalable Databricks-based data pipelines and ELT workflows; develop and document dbt models; define data modeling methodology; ensure data quality, reliability, and scalability; collaborate with clients and cross-functional teams; provide technical guidance and deliver improvements to clients' data infrastructure and analytics capabilities.

Build and own the reporting and insight layer: write optimized Redshift queries, build dashboards, monitor model feedback signals, triage and prioritize investigations for data science, maintain ETL pipelines ingesting semi-structured data, and enable Customer Success with clear metrics and onboarding checks. Move into model validation and automation to scale analytic workflows.