Top Analytics Engineer Jobs in NYC, NY

Architect and own Samsara’s marketing data and AI foundation, including Databricks pipelines, data models, semantic layers, and self-service analytics infrastructure. Lead cross-functional initiatives, ensure data quality and reliability, build and evaluate production LLM and agent systems, automate workflows with AI, and mentor engineers and analysts. Partner with Marketing, Sales, BI, and Data Science teams to turn ambiguous business needs into scalable data products.

Build the company’s analytics engineering function and trusted data foundation. Develop production analytical datasets, reusable models, semantic layers, canonical metrics, and self-service analytics capabilities. Support product analytics, experimentation, causal analysis, marketplace diagnostics, and automated workflows. Partner with Product, Finance, Measurement, Data Engineering, and business teams to establish consistent definitions and reproducible insights. As the founding hire, this role will also help hire and develop future analytics engineers.

Build data and semantic models, documentation, context, and evaluations that improve conversational analytics powered by LLMs. Embed with stakeholder teams to understand analyst workflows and business needs, then develop self-service tooling and standardized analytic frameworks. Train non-data stakeholders on effective data and LLM usage, create reusable training materials, maintain evaluation processes, and track conversational analytics accuracy. Requires strong SQL, dbt modeling, BI, communication, teaching, problem-solving, and project ownership skills.

Lead research and engineering for risk scoring and prioritization in Exposure Management. Architect models, productionize algorithms with engineers, enable observability, set technical standards, mentor team members, and represent technical direction externally.

Own the architecture and standards of the company’s dbt project, building scalable models across staging, intermediate, and mart layers. Partner with product, design, and engineering teams to evolve data models, validate metrics, and maintain data quality. Develop AI-assisted testing, documentation, and access workflows; collaborate on Segment, Fivetran, and Airflow pipelines; and drive privacy, governance, and warehouse best practices.

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.

Leads data and analytics engineering across cross-functional teams, translating business needs into technical solutions, managing data models and context layers, and ensuring data quality, governance, and accessibility. Establishes innovation environments, develops training and cross-skilling programs, mentors data professionals, supports self-service analytics and AI capabilities, and drives continuous improvement, technical reviews, and strategic data initiatives.

Develops AI-driven Celonis prototypes and proof-of-concept solutions using SQL, PQL, machine learning, and LLMs. Consults with customers to identify business challenges, presents value propositions, quantifies ROI, leads platform demonstrations, and builds trusted advisor relationships. Partners with sales, implementation, product, and engineering teams to deliver value realization and reusable best practices through the 12-month Orbit Program.

Build Deepgram’s governed decision layer above warehouse gold tables. Responsibilities include modeling cross-system entities, owning semantic and metrics definitions, creating agent-facing data tools and MCP surfaces, implementing data quality assertions and reconciliation checks, evaluating AI agent accuracy, and answering business questions while leaving reusable models behind. The role requires production-grade SQL and Python, semantic-layer ownership, strong data modeling judgment, AI-native workflows, clear writing, and comfort building infrastructure from scratch.

Own and maintain dbt data models across design, testing, deployment, monitoring, and documentation. Translate ambiguous Marketing and Product needs into reliable data solutions, write design documents, support production issues, create runbooks and schema documentation, and coach junior engineers. The role uses SQL, Python, Snowflake, dbt, Dagster, and AWS while partnering closely with Marketing, Data, and MarTech teams.

Own analytics engineering for the machine infrastructure supply chain team. Build and maintain semantic layers, trusted data models, metrics, dashboards, and analytics-ready datasets using dbt and modern data platforms. Analyze inventory trends, optimize stock levels, investigate discrepancies, and develop supply chain KPIs. Partner with data engineering, finance, product, operations, planners, and buyers while improving inventory planning, replenishment, ERP processes, and warehouse efficiency.

Own end-to-end analytics engineering for AI and developer-experience telemetry, including instrumentation, production pipelines, governed metrics, monitoring, executive dashboards, and analysis. Define and validate new measures of AI productivity and ROI, communicate findings to senior stakeholders, and recommend improvements to tooling, processes, and investment. Collaborate with data engineering, applied AI, and data science teams while using AI coding agents to build and maintain pipelines and reporting.

Build and maintain marketing data pipelines connecting Salesforce, Marketo, and cloud data warehouses. Automate campaign reporting, develop SQL datasets and views, improve attribution and data quality, support CRM and marketing automation configuration, and document data models and processes. Use AI tools such as Claude to accelerate analytics workflows while ensuring reporting accuracy. Partner with Marketing Analytics, Demand Generation, and Marketing Operations to support executive-level pipeline reporting.

Own AI and developer-experience analytics end to end, including telemetry instrumentation, production data pipelines, metric governance, executive dashboards, and analysis. Define trustworthy metrics from evolving signals, monitor pipeline quality, communicate insights to senior stakeholders, and recommend actions to improve AI ROI and developer productivity. Use AI coding agents to build and maintain pipelines, dashboards, and analyses while partnering with data engineering, applied AI, and data science teams.

Contract Analytics Engineer maintaining Looker and LookML dashboards, reporting, data operations, sales pacing, data QA, vendor workflows, board reporting, forecasting, and planning support during a director’s maternity leave. The role requires strong SQL and Snowflake skills, analytics engineering experience, vendor management, Git/GitHub proficiency, documentation, and independent work in a lean data team.

Build and maintain Warp’s analytics infrastructure, canonical data models, ETL and reverse-ETL pipelines, data quality systems, and self-service analytics tools. Define product and business metrics, analyze experiments, support pricing and revenue analytics, and conduct research into user behavior, growth, engagement, and churn. The role partners broadly across product, engineering, growth, revenue, and go-to-market teams while incorporating AI agents into data workflows.

Designs and maintains credit loss models, analytical solutions, data workflows, automated pipelines, reports, and dashboards supporting CECL, stress testing, underwriting, reserving, forecasting, and risk management. Partners with Finance, Risk, IT, Data Management, and business teams to translate requirements into scalable solutions. Documents model development, testing, governance, and regulatory compliance activities while addressing data quality and reporting challenges.

Design and implement scalable analytics/data models and dbt pipelines, manage Databricks, build dashboards and BI solutions, partner with business stakeholders, support CRM/marketing analytics, and explore GenAI/ML opportunities to deliver reliable, automated data products.

Build and maintain scalable ELT pipelines, dbt models, semantic layers, and governed product analytics assets. Partner with Data Engineering, Product Engineering, Data Governance, analysts, and product managers to define trusted metrics, monitor data quality, document taxonomies, and support self-service analytics. Translate product usage telemetry into certified metrics for expansion and upsell analysis, while communicating technical concepts to technical and non-technical stakeholders and driving engineering best practices.

Lead end-to-end analytics for Netflix Ads demand, including media planning and campaign management tools, machine learning models, and agentic solutions. Partner with scientists, product managers, engineers, and data engineering to develop metrics, reliable analytics data, insights, and recommendations. Independently identify risks and opportunities, shape product roadmaps, and communicate findings to technical and non-technical stakeholders.

Build modern analytics infrastructure, self-service AI analytics tools, foundational datasets, and business data pipelines. Partner with teams to track product metrics, support financial and marketing reporting, identify cost savings, and improve business decisions through strategic analysis and consulting.

Build and expand the company’s analytics data stack using Looker, LookML, dbt, BigQuery, and Airbyte. Develop data models, integrate multiple business systems, create KPI dashboards, and analyze product usage, customer health, adoption, and revenue data. The role empowers business teams with self-service analytics, actionable insights, and data-informed decision-making while developing a strong understanding of business metrics.

Own the customer experience data foundation by designing dbt models, pipelines, data quality tests, documentation, semantic layers, dashboards, and self-service datasets. Integrate support, KYC, withdrawal, CRM, workforce management, and market resolution data into the cloud warehouse. Identify recurring support issues, communicate findings to stakeholders, and partner with data engineering on infrastructure and governance.

Designs and governs scalable dbt data architecture, analytics models, testing, documentation, metadata, and semantic-layer standards. Builds agent-safe infrastructure and self-service analytics experiences in Hex, improves data quality and reliability, and partners with engineering, data science, and business teams. The role also influences roadmap decisions, mentors teammates, and develops reusable tooling and frameworks for trusted, cost-efficient cloud data products.

Build and maintain production-grade ETL/ELT pipelines, financial data models, integrations, monitoring, testing, and automation. Partner with Accounting, Finance, Engineering, and Data Infrastructure to improve reporting accuracy, data reliability, and business insight. Lead data strategy, roadmap planning, workflow automation, and GenAI-enabled productivity initiatives across Finance.