Analytics Engineer Remote Jobs in New York (US)
This page tracks remote analytics engineer openings that are location-eligible for New York.
This page tracks remote analytics engineer openings that are location-eligible for New York.
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529 Jobs
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Hospitals and healthcare services in Indianapolis, Lafayette, northwest and western Indiana and south-suburban Chicago.
• Designing and creating tables within data marts, data lakes and data warehouses • Building systems that collect, manage, and convert raw data into usable information for analytics • Expanding and optimizing data and data pipeline architecture • Mentoring junior data engineers and promoting data education
Helping students become fearlessly curious through AI-powered online discussion. TIME World's Top EdTech Companies 2024
• Consult with teams across the organization to translate key product and business processes into technical data requirements. Use dbt, BigQuery, and SQL to build up Packback’s metrics layer to enable self-serve access to core analytics. • Build, optimize, and orchestrate data pipelines with dbt, Airflow, and Python • Establish data integrity standards and SLAs to ensure timely, accurate delivery of data • Build insightful and reliable dashboards to track performance of core company metrics across the product and business • Collaborate with business operations teams to define and train on best practices with respect to data modeling, analytics, and visualization • Collaborate with leadership to conduct analyses and apply statistical methods to answer day-to-day questions or support larger initiatives • Work within Packback’s research function with partner institutions and perform in-depth analyses to understand how Packback’s products are driving key student outcomes • Consult on the adoption of new technologies that impact Packback’s data infrastructure
• Own the analytics modeling lifecycle for a business domain (GTM / Lead-to-Customer or Post-Sales) — build and maintain dbt models and business marts on top of our source data in Snowflake. • Develop and maintain the semantic layer that powers our BI platform. • Partner directly with stakeholders across Finance, Sales, Marketing, CS, and RevOps to translate ambiguous business questions into the right model — prescribing or designing one when none exists. • Define, document, and enforce consistent metric definitions and segmentation across the org (e.g., customer lifecycle stages, production lifecycle, ARR), establishing whether each lives in dbt or the semantic layer so metrics aren't defined twice. • Build trustworthy self-serve data products so business partners can answer routine questions without help. • Collaborate with the Data Platform team where raw data is handed off — specify and request new sources, validate data, and raise quality issues upstream.
We create honest financial products that improve lives.
• Build and operate core platform capabilities: Design and implement platform features that enable engineers across Affirm to build, deploy, and operate data applications at scale — covering automated provisioning, deploy pipelines, access control, service lifecycle management, and reliability tooling. • Develop integrations: Build and maintain integrations between the platform and internal systems — CI/CD pipelines, identity and secrets management, external APIs, and event-driven hooks — so that application teams have secure, reliable, and self-service access to the tools they need. • Strengthen data access and governance: Design and operate secure data access patterns across the platform's analytical infrastructure, including RBAC, managed-access schemas, dynamic data masking, secure views, and cross-database grants that make platform data trustworthy without creating operational bottlenecks. • Automate toil and unlock self-service: Identify recurring support patterns — access provisioning, data pipeline failures, service health management, secrets management — and build the automation and tooling to make them self-service for platform users, eliminating manual admin work. • Improve observability and incident response: Build telemetry, alerting, and log infrastructure so platform teams and application owners can diagnose and resolve issues independently. Establish runbooks and operational patterns that reduce blast radius and recovery time. • Support platform operations: Participate in on-call coverage, triage production incidents across platform services and the applications that run on them, and execute platform-level maintenance with care for the engineers and workflows that depend on the platform. • Contribute to the emerging platform roadmap: Help design and build next-generation platform capabilities — semantic layer infrastructure, agentic application patterns, and AI-native data tooling — as the platform grows to support Affirm's most ambitious data use cases. • Raise the engineering bar: Set and hold high standards in code review, design, operational readiness, and incident follow-through. Help establish patterns that other contributors can follow and build on.
DevSecOps and AI from Cloud to Mission Edge | Kubernetes Partner | Multicloud | 8(a) | HUBZone
• Design, build, and maintain the data storage and integration components of a mission-critical federal program. • Ensure enterprise databases are well-designed, high-performing, and reliable to support data integration objectives. • Design and implement database schemas and features, including complex SQL queries, stored procedures, and database code. • Monitor and optimize database performance through query tuning, indexing, and resource management. • Perform database optimization, performance tuning, and configure high-availability solutions to support large data volumes. • Oversee routine maintenance tasks such as backups, recovery procedures, and patching. • Implement and maintain robust disaster recovery strategies and backup policies. • Enforce database security controls, including access management, encryption, and hardening procedures. • Collaborate closely with data engineers and integration developers to ensure database structures support ETL/ELT processes. • Validate that database schemas align with incoming data feeds and ensure smooth operation of interfaces used for data integration.
Location: Remote (eligible US states: California, Colorado, Illinois, Massachusetts, New Jersey, New York, Ohio, Oregon, South Carolina, Texas, or Washington) Compensation: $140,000-175,000, commensurate with experience, plus bonus and benefits Chief Detective Chief Detective is a growth-focused agency and deep brand partner for D2C brands, especially in e-commerce. We build the analytics, pipelines, reporting, & web-apps our team and our clients actually make decisions on. Clean, well-modeled data here does not just feed dashboards, it empowers every employee and client to better achieve their goals. We move fast and care about measurable impact. About the Role We are hiring an AI-forward Senior Data & Analytics Engineer to own the data foundation everything else at Chief Detective is built on: the BigQuery and GCP data layer, the dbt models, and the reporting our team and clients depend on. This role sits where analytics engineering meets product. You will build and own the foundation, and you will help turn it into the AI workflows and web apps we ship. The data you model here does not just feed dashboards, it powers the tools that our media-buying, creative, executive, and client teams rely on every day. This is a hands-on, technically rigorous seat for someone who likes being in the weeds and making an impact end to end, from raw source data through the models and pipelines to the AI and products on top. What You'll Do - Own our dbt models and the BigQuery data layer end to end: design, test, document, schedule, and ship clean, reliable data from staging through marts following best practices - Build and maintain the pipelines that bring marketing, e-commerce, and fulfillment data into our warehouse, and stand up custom extractions when off-the-shelf connectors fall short - Be the bridge between data and the business: turn questions from across the company into trusted KPI definitions, Looker Studio reporting, and reusable data models - Model e-commerce data for forecasting and lightweight predictive work, including demand and revenue forecasting, regression, and correlation, where it drives real decisions - Build and maintain the clean serving layer our AI workflows, agents, and internal web apps query, in place of direct API calls - Use AI daily: write dbt models and debug pipelines with Claude Code and Cursor, and build practical AI workflows on Google's AI stack (Gemini and the Gemini Enterprise Agent Platform, formerly Vertex AI) for enrichment, QA, classification, and automation - Help build our own web apps and products (React, Next.js) on the same GCP and BigQuery data layer - Keep our GCP environment running safely (IAM, service accounts, secrets, serverless) and keep a pulse on new GCP and AI capabilities so we stay ahead of the game What We're Looking For - 5+ years in analytics engineering or data engineering on a modern data stack - Strong SQL (joins, window functions, CTEs) with hands-on, production experience in BigQuery on GCP - Proven dbt ownership in production: modeling, testing, documentation, and job scheduling in dbt Cloud or an equivalent setup - Python proficiency for automation services, custom API integrations, and light modeling - Hands-on experience with marketing and e-commerce analytics data: ad platforms, Shopify, and GA4-style event data - Strong debugging ability: you can trace a "this dashboard is wrong" issue back through reporting, models, pipelines, and source data - Daily use of AI-assisted coding and agentic tools (Claude Code, Cursor, or comparable) - Comfortable operating in a GCP environment (IAM, service accounts, secrets, serverless) - A strong communicator who can work with non-technical and client stakeholders, translate business questions into durable data, and manage multiple priorities - Solid Git, pull-request, and documentation habits (runbooks, metric definitions, system notes) Nice to Have - At least one production workflow built on LLM APIs or comparable AI services (Gemini, Vertex / Gemini Enterprise Agent Platform, or similar), beyond prompt experimentation - Front-end or product engineering experience (React, Next.js) and interest in helping build our web apps and products - Statistical modeling and predictive analytics (regression, time series, correlation) on e-commerce or marketing data - Looker Studio tuning with cost and performance awareness, deeper GCP ops (Cloud Run / Cloud Functions, Cloud Scheduler / Workflows, Pub/Sub), or data observability patterns (freshness SLAs, alerting, anomaly detection) - Relevant certifications such as Google Cloud Professional Data Engineer - Appetite to mentor contractors or junior developers and grow into broader ownership of the analytics function as the team scales - Equivalent practical experience in place of a formal degree is fully respected Benefits - Competitive salary: $140,000-175,000, commensurate with experience - Comprehensive benefits: group medical, dental, and vision coverage; short- and long-term disability; life insurance; 401(k) eligibility after one year of service with matching contributions; paid time off; sick leave; an Employee Assistance Program, and more - Professional growth: the opportunity to shape our analytics future, build durable systems, and grow quickly with a sharp, entrepreneurial team - Innovative culture: work closely with leadership and a team focused on building practical, high-impact analytics systems If you are excited to own a modern analytics foundation, improve data reliability, and build useful automation and AI on top of it, we would love to hear from you.
Merit America closes the opportunity gap at scale by preparing adults stuck in low-wage roles for well-paying careers.
• Own and improve canonical data models • Own and evolve the dbt models that transform raw source-system data into durable, reporting-ready tables. • Improve models over time by reducing duplication, clarifying grain, and documenting business logic. • Turn recurring reporting needs into reusable models rather than one-off queries. • Identify opportunities to simplify the data model and make reporting easier to understand and maintain. • Own the Lightdash semantic layer so core metrics are defined consistently and documented where they're used. • Maintain source-of-truth definitions, promoting and deprecating metrics as the business changes. • Improve the reporting ecosystem over time by making analytics more self-service and easier to use. • Build and maintain observability, testing, and monitoring across the analytics stack so data quality issues are identified early and resolved across source systems, transformations, and BI outputs. • Maintain strong development practices: version control, PR review, documentation, testing • Review analytics work for modeling quality and maintainability, and help analysts use the stack effectively. • Cross-train teammates so knowledge is shared rather than concentrated.
Discover a career where you can Create Your 𝗣𝗼𝘀𝘀𝗶𝗯𝗹𝗲™.
Role Description We are seeking a highly analytical and technically skilled Senior Digital Analytics Customer Experience (CX) Engineer to join our Global Digital Analytics Customer Experience (CX) team, supporting the Performance Coatings Group’s digital initiatives. This role is responsible for designing, implementing, and optimizing digital data collection strategies across websites, applications, and digital marketing channels. The ideal candidate brings deep experience in digital analytics, strong data engineering and data governance expertise. This candidate is expected to use these skill sets to help elevate customer experiences, build a foundation for personalization and digital marketing activation, and drive business growth. As a senior member of the team, this person will play a critical leadership role in ensuring data quality, standardization, and connectivity across digital platforms, while advancing our measurement framework, analytics automation, and predictive insights capabilities. Qualifications - Deep experience in digital analytics - Strong data engineering and data governance expertise Requirements - Contact with other employees and access to confidential and proprietary information - Review of criminal history may be necessary to protect the business and its operations Benefits - Life … with rewards, benefits and the flexibility to enhance your health and well-being - Career … with opportunities to learn, develop new skills and grow your contribution - Connection … with an inclusive team and commitment to our own and broader communities Company Description At Sherwin-Williams, our purpose is to inspire and improve the world by coloring and protecting what matters. Our paints, coatings and innovative solutions make the places and spaces in our world brighter and stronger. Your skills, talent and passion make it possible to live this purpose, and for customers and our business to achieve great results. Sherwin-Williams is a place that takes its stability, growth and momentum and translates it to possibility for our people. Our people are behind the strength of our success, and we invest and support you in: - Life … with rewards, benefits and the flexibility to enhance your health and well-being - Career … with opportunities to learn, develop new skills and grow your contribution - Connection … with an inclusive team and commitment to our own and broader communities At Sherwin-Williams, part of our mission is to help our employees and their families live healthier, save smarter and feel better. This starts with a wide range of world-class benefits designed for you. A general description of benefits offered can be found at http://www.myswbenefits.com/ . Click on “Candidates” to view benefit offerings that you may be eligible for if you are hired as a Sherwin-Williams employee.
• Design, build, and optimize complex Power BI semantic models, advanced DAX measures, and executive-grade dashboards • Develop and tune dbt models and SQL transformations against Snowflake and other modern cloud data platforms • Own end-to-end data pipelines that consolidate CRM, ERP, product, and financial data into trusted, reusable layers • Diagnose and resolve performance, accuracy, and usability issues in existing reporting independently • Establish and enforce semantic modeling patterns, KPI definitions, naming conventions, and code review practices • Mentor mid-level and junior analytics team members through pairing, review, and documentation • Partner with the Director to shape the analytics architecture and reduce technical debt over time • Champion governance and data quality controls without slowing the team down • Work directly with RevOps and FP&A stakeholders to define metrics, forecasting views, and performance frameworks • Translate evolving requirements into intuitive, high-quality visualizations leaders trust and use • Present findings clearly to technical and non-technical audiences alike
Revolutionizing the way healthcare is delivered to babies, children, and young adults.
Role Description The AI & Analytics Engineer I supports the design, build, and delivery of user-facing AI-powered applications, data pipelines, and analytics solutions that drive operational efficiency and decision-making across the organization. This is a developing-professional role focused on hands-on execution under the guidance of senior engineers. Core Responsibilities - AI Applications Development - Assist in building and enhancing AI-powered applications and agents that support business workflows. - Build user-facing interfaces using a modern frontend framework (React, Vue, Angular, or similar). - Develop backend services and REST or GraphQL APIs (Python, Node.js, .NET, or similar). - Develop components of AI solutions that automate routine tasks and surface insights. - Gather requirements from stakeholders with guidance from senior team members. - Iterate on AI applications based on user feedback and testing results. - Provide ongoing Level 3 support for software products. - AI Integration & Delivery - Support integration of AI applications with enterprise systems under senior direction. - Assist with deployment, testing, and monitoring of AI solutions in lower and production environments. - Translate documented business requirements into functional workflows. - Follow established standards for reliability, security, and code quality. - Data Engineering & Pipeline Development - Build and maintain ETL/ELT pipelines that feed analytics and AI use cases. - Ingest and transform data from multiple source systems into centralized platforms. - Validate accuracy, completeness, and structure of pipeline outputs. - Analytics & Data Modeling - Develop and maintain semantic models, datasets, and dashboards (Power BI and related tools). - Apply standardized business metrics and KPI definitions across reports. - Optimize queries and data structures for performance and usability. - Implement and maintain row-level security and access controls on reports. - Cross-Functional Collaboration - Partner with IT, clinical informatics, operations, and business stakeholders to understand reporting and AI needs. - Communicate progress, blockers, and trade-offs clearly to both technical and non-technical audiences. - Escalate architectural or scope questions to senior engineers. - Engineering Standards & Quality - Follow team practices for source control, code review, documentation, and testing. - Monitor AI outputs and data pipelines for accuracy and reliability. - Support compliance with data security, privacy, and governance standards (HIPAA-aware). - Continuous Learning & Improvement - Build technical depth in AI/ML tooling, cloud services, and modern data platforms. - Contribute small improvements to existing AI tools, dashboards, and pipelines. - Stay current with emerging AI and analytics technologies relevant to healthcare operations. Scope & Impact - Contributes directly to AI application and analytics delivery used across business operations. - Expands team throughput on AI applications, dashboards, and insight distribution. - Operates under the technical direction of the AI & Analytics Engineer II and Senior Director of AI, Data, & Enterprise Applications. Success Metrics - Volume and quality of analytics and AI deliverables completed. - Reliability and performance of owned pipelines and reports. - Reduction in backlog for analytics and AI requests. - Growth in technical proficiency over the first 12–18 months. - Stakeholder satisfaction with delivered solutions. Qualifications - 2–4 years of experience in data analytics, BI development, data engineering, or software development. - Hands-on experience building custom software products, including writing code and delivering end-to-end solutions aligned to business requirements. - Proven ability to conduct testing, troubleshoot and debug issues, and ensure high-quality, reliable application performance across environments. - Hands-on experience building dashboards, reports, or data pipelines in a professional setting. - Hands-on experience building and shipping user-facing applications end-to-end, including both frontend and backend components. - Demonstrated proficiency with a modern frontend framework (React, Vue, Angular, or similar). - Demonstrated proficiency with a backend framework (FastAPI, Django, Express, ASP.NET Core, Spring Boot, or similar). - Active GitHub profile or equivalent code portfolio with reviewable code samples (required at application). - Exposure to AI, machine learning, or workflow automation projects (academic or professional) preferred. Requirements - Demonstrated proficiency in modern software engineering tools and practices, including Agile development, CI/CD workflows, automated testing, and code review standards. - Daily experience with Git and GitHub, including branching, pull requests, and code review workflows. - Working proficiency with SQL and relational data modeling. - Experience with Power BI (or comparable BI platform) including DAX and semantic models. - Familiarity with ETL/ELT concepts and at least one data integration tool. - Exposure to cloud platforms (Azure preferred) and AI/ML services a plus. - Comfort working with both structured and unstructured data. Competencies - Strong analytical and problem-solving skills with attention to detail. - Willingness to learn from senior engineers and apply feedback quickly. - Effective written and verbal communication with technical and non-technical partners. - Ability to manage multiple concurrent tasks and meet committed delivery dates. - Healthcare or pediatrics domain interest is a plus. Compensation The salary/rate range listed here has been provided to comply with local regulations and represents a potential base salary/rate for this role. Please note that actual salaries/rates may vary within this range above or below, depending on experience and location. EEO Statement PM Pediatric Care is an equal employment opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, disability status, protected veteran status or any other characteristic protected by law.
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SQL, Cloud, Azure, ETL, Python, AWS