Data Analytics Engineer in New York

The mission of The New York Times is to seek the truth and help people understand the world. That means independent journalism is at the heart of all we do as a company. It’s why we have a world-renowned newsroom that sends journalists to report on the ground from nearly 160 countries. It’s why we focus deeply on how our readers will experience our journalism, from print to audio to a world-class digital and app destination. And it’s why our business strategy centers on making journalism so good that it’s worth paying for. About the Role, Mission or Department Overview We are looking for a Senior Program Manager to shape and lead our People Data & Insights team by turning complex HR questions into scalable, automated solutions. In this high-visibility role, you will partner directly with HR leadership to create our roadmap while managing the end-to-end lifecycle from intake to delivery. You will spend equal time translating business needs with stakeholders and partnering with engineers on technical execution. Beyond building tools, you will own the critical final mile by driving adoption and ensuring leaders understand and trust the insights we deliver. You will report to our Senior Director, People Data & Insights. Responsibilities: You will handle sensitive people data and own the roadmap for people data solutions, from intake through launch, and adoption You will oversee request intake and the prioritization process ensuring requesters understand what is being built, what is not, and why You will translate business questions into solution requirements You will partner daily with the Analytics Engineer and Solutions Architect on scoping, sequencing, and delivery You will build the education layer into the solution itself. You will partner on data governance, contribute to the data dictionary, and hold the line on metric definitions You will manage stakeholders across HR, Technology, Legal, and the business, including senior leaders. You will demonstr

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. Fraud Data is the data science and machine learning team within Plaid’s Fraud organization, responsible for using data and ML to improve and scale Plaid’s fraud products. Within Fraud Data, the Customer & Product Intelligence team focuses on understanding product performance, uncovering customer insights, and enabling go-to-market teams with data-driven solutions. The team partners closely with customers and GTM teams on fraud analyses and proofs of concept, turning customer learnings into scalable, reusable product capabilities. We also build the metrics, analytics, and data foundations that measure product health, identify opportunities for improvement, and guide product decisions across Plaid’s Fraud portfolio. As a Data Science Manager, you will lead a team responsible for customer-facing data science and Fraud product analytics. You will set the team's roadmap, develop its data scientists, and remain involved in analytical methods, technical reviews, and customer investigations. You will: Set a 6–12-month roadmap with Product, Engineering, and GTM, and assign priorities and responsibilities across the team. Define product metrics, their underlying data, and reporting and

CLEAR is building THE secure identity company of the future. Our mission is to make experiences safer and easier—physically and digitally. With more than 43 million Members and a growing network of partners across the world, CLEAR's secure identity platform is transforming the way people live, work, and travel. Whether it’s at the airport, stadium, or throughout your everyday life, CLEAR unlocks the magic of frictionless experiences. We’re looking for a Data Engineer II to help us build the next generation of products which will go beyond just ID and enable our members to leverage the power of a networked digital identity. As a Data Engineer at CLEAR, you will participate in the design, implementation, testing, and deployment of applications to build and enhance our platform- one that interconnects dozens of attributes and qualifications while keeping member privacy and security at the core. A brief highlight of our tech stack: SQL / Python / Looker / Snowflake / dbt What you'll do: Build a scalable data system in which Analysts and Engineers can self-service changes in an automated, tested, secure, and high-quality manner Build processes supporting data transformation, data structures, metadata, dependency and workload management Develop and maintain data pipelines to collect, clean, and transform data (owning end to end data product from ingestion to visualization) Develop and implement data analytics models Partner with product and other stakeholders to uncover requirements, to innovate, and to solve complex problems Have a strong sense of ownership, responsible for architectural decision-making and striving for continuous improvement in technology and processes at CLEAR What you're great at: 4+ years of data engineering experience Working with cloud-based application development, and be fluent in at least a few of: Cloud services providers like AWS Data pipeline orchestration tools like Airflow, Dagster, Luigi, etc Big data tools like Spark, Ka

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. About Modal Data: We’re growing our Data team and are looking for our first few key hires to build self-serve data tools and drive business strategy in the right direction. The mission of the Modal Data team is to make it easy to track company goals, make evidence-backed decisions, and prioritize the right work. We do this via: Self-serve AI analytics tools (Hex, Snowflake) Embedding with teams as a “data adviser”, providing strategic analysis and consulting What You'll Do: Contribute to building the most modern analytics stack in Data today to support AI-driven self-serve analysis, key metrics tracking, and external customer reporting Influence work on new products like LLM Inference Endpoints through product analytics tracking Identify millions of dollars of cost savings and optimization across our tools and financial operations Write data pipelines that power the operatio

You will lead a small, hands-on engineering team building the secure, scalable Core Analytics Data Access Platform that accelerates Datadog’s Applied AI and analytics capabilities. The team owns the Data Access Platform — a unified interface that lets AI and analytics teams discover and self-serve production-ready datasets while abstracting underlying systems and embedding required legal and compliance guardrails. In this role you’ll own technical direction, contribute to design and code, and partner closely with Applied AI, Product Analytics, and internal platform teams to provide reliable datasets and APIs for model training and analysis. This role balances day-to-day engineering leadership with long-term platform planning. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do: Lead a Hands-On Engineering Team: Manage, mentor, and grow a small team of 2–4 data engineers (mix of senior and junior) across Paris and NYC, fostering technical excellence and career development. Own Technical Direction and Delivery: Define architecture, engineering priorities, and the team roadmap for the Data Access Platform, driving implementation of scalable, secure data pipelines and platform services. Contribute to Design and Code: Spend substantial time coding, reviewing, and shipping critical platform components to ensure performance, reliability, and operational excellence. Partner with Internal Stakeholders: Work closely with Applied AI, Internal Product Analytics, product managers, and platform teams to define data contracts, APIs, SLAs, observability, and curated analytical datasets. Ensure Data Security, Governance, and Reliability: Implement access controls, lineage, monitoring, and compliance guardrails to support safe model training and repeatable analytics workflows.

The mission of The New York Times is to seek the truth and help people understand the world. That means independent journalism is at the heart of all we do as a company. It’s why we have a world-renowned newsroom that sends journalists to report on the ground from nearly 160 countries. It’s why we focus deeply on how our readers will experience our journalism, from print to audio to a world-class digital and app destination. And it’s why our business strategy centers on making journalism so good that it’s worth paying for. About the Role New York Times Wirecutter team is looking for a Senior Manager, Audience & Product Analytics to power our growth strategy. Your core mandate will be to build an understanding of our audience— who they are, how they engage with content and product features, and what brings them back. You will lead a team of analysts responsible for understanding audience trends, driving product improvement, and optimizing user engagement. You will provide leadership and data-driven insights, working with audience, product, and engineering leads to establish goals, design experiments, and uncover new opportunities. You will report to the Executive Director of Data Analytics and Insights, Wirecutter. This opportunity is flexible to in office or remote work. Responsibilities You will manage a team of three direct reports—promoting a culture of curiosity and excellence while setting clear deliverables, supporting career growth, and ensuring high-quality analytics output You will analyze audience growth, traffic drivers, customer journey touch points, and user engagement trends to inform strategic decision-making. You will partner with functional leads (Product, Design, Engineering, Editorial) to set strategy, define goals, and build the audience and product analytics roadmap. You will lead the design, execution, and post-analysis of product experiments (A/B testing). Provide expertise on primary metrics, trade-offs between competing goal

The Internal Product Analytics (IPA) team is the analytics backbone of Datadog's Product organization. With over thirty products on a single platform, IPA gives PMs and leadership the data, tooling, platform, and analysis they need to make good decisions. The team owns the recurring analytical work the Product org runs on and builds the AI-first workflows that make that analysis faster and more consistent across the org. As Manager of the Internal Product Analytics team, you will directly manage a team of data analysts, partner closely with the PMs your team serves, and partner with the associated platform engineering teams. You will set the direction for how the team delivers analysis, builds AI-first tooling, and partners across functions. This is a role for someone who works well across teams, turning complex data and open questions into clear, trusted answers that PMs and leadership can act on. What You'll Do: Guide and grow the Internal Product Analytics team. Manage, coach, and develop a team of data analysts. Set priorities, hold a high bar for quality, and make sure the team's output is trusted across the Product org. Partner directly with the PM org. Work side by side with the PMs your team serves to frame the questions that matter, shape the analysis, and make sure the answers reach them in a form they can act on. Own the recurring analytics the PM org runs on. Business reviews, feature request analysis, usage and adoption tracking, and pricing analysis. Make this work consistent, repeatable, and fast so PMs get answers when they need them. Build AI-first analytics. Design and ship AI-powered workflows and agents that do the heavy lifting of analysis, from data querying to synthesis to reporting. Set the standard for how the team uses AI so analysis scales without simply adding headcount. Partner across functions. Work with Finance, Data Platform, Engineering, and Revenue teams to align on definitions, source the right data, and turn raw signals into decis

Manager I, Engineering - Onboarding Growth This Engineering Manager will lead the Onboarding team within the Shared Capabilities organization, setting product direction, and coaching and developing team members. They will staff and drive projects that drive new users to adopt Datadog in their organizations. From the first time a new user signs up for a Datadog trial, this team is responsible for making sure they have a world class setup and onboarding experience, and can realize the full value of Datadog in their organization. Onboarding owns the “front door” of Datadog, the very first experience new users and prospective customers encounter. This work is extremely important to company success - improving our onboarding process directly impacts new users ability to find value with Datadog quickly. This role also involves close partnerships with other product teams to deeply understand customer needs and deliver compelling first-time user experiences At Datadog, we place value in our office culture - the relationships that it builds, the creativity it brings to the table, and the collaboration of being together. We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them. What You’ll Do: Work with Product Management and Design to plan and staff projects for the Onboarding Growth team Coach and develop engineers at various levels Ensure strong cross-team communication and design best practices around project management, code review, architecture patterns, and more Build UX features to streamline the onboarding and trial experience through rapid experimentation Build out data platforms and use that data to personalize the onboarding experience for each user Work directly with product teams to ensure each product has a best in class new user experience Create AI-assisted setup options to make sure initial setup “just works” out of the box Champion the use of analytics data to analyze user behaviour and apply those insights

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We're looking for strong backend engineers who love building a developer tools used by the largest AI companies in the world. You’ll be building for things at scale, but also for new AI workflows that change every day. Requirements: Experience building and shipping modern web applications end-to-end. We care more about what you’ve built than how many years you’ve been building. Comfort working across the stack: TypeScript on the frontend, Python services on the backend, and ClickHouse for data and analytics. Deep knowledge of observability tools and patterns used for large-scale workloads such as custom sandboxes, training and inference for large language (LLM) and diffusion models. Experience with at least one of: billing/payments systems, B2B SaaS tooling, or enterprise software, or LLM / diffusion models inference and training loads. Strong product instincts; yo

About the Company Sigmoid enables business transformation using data and analytics, leveraging real-time insights to make accurate and fast business decisions, by building modern data architectures using cloud and open source. Some of the world’s largest data producers engage with Sigmoid to solve complex business problems. Sigmoid brings deep expertise in data engineering, predictive analytics, artificial intelligence, and DataOps. Sigmoid has been recognized as one of the fastest growing technology companies in North America, 2021, by Financial Times, Inc. 5000, and Deloitte Technology Fast 500. Job Description As an Account leader you will be responsible for ensuring customer success and growth in Fortune 1000 companies working with Sigmoid. A maverick self-starter, you understand brand building, how to sell innovation, drive deals forward and compress decision cycles. You will play a key role in driving our business to great heights, and drive our revenue growth in parallel. We're looking for a passionate farming growth hacker with a track record of proven success in Data Solutions selling in Fortune 1000. Prior experience in Analytics, Data Science & Big Data will be an added advantage. Job Responsibilities As an Account leader, you will have the opportunity to work on major business initiatives that contribute to Sigmoid’s growth and productivity objectives. In this role, you will have the responsibility of managing multiple account management strategy implementation assignments supporting the Account Management function and will work directly with the business, IT and strategy teams in catering to the end-to-end business needs. Essential Responsibilities Manage account management strategy implementation and validation. Effective communication and presentation ability. Ability to work as in a team as well as contributing as an individual. Lead and provide a road map for account. Able to establish priorities and coordinate work. Evaluate, scrutinize and str

About DataCamp DataCamp's mission is to empower everyone with the data and AI skills essential for 21st-century success. By providing practical, engaging learning experiences, DataCamp equips learners and organizations of all sizes to harness the power of data and AI. As a trusted partner to over 20 million learners and 6,000+ companies , including 80% of the Fortune 1000, DataCamp is leading the charge in addressing the critical data and AI skills shortage. About the role + Responsibilities The Learning Solutions Architect is a customer-facing technical trainer who helps organizations build data and AI capabilities with DataCamp. You’ll deliver engaging live training (virtual and onsite) and create tailored learning materials on the DataCamp platform—think custom courses, projects, certifications and assessments that map to each customer’s roles, tools, and outcomes. You’ll partner with Customer Success, Sales, Product and Content to scope blended learning paths, teach high-impact sessions, and publish reusable, high-quality content that accelerates adoption and measurable skill uplift for some of our most high-value customers. Qualifications Loves to teach. Comfortable spending multiple hours weekly in front of a virtual or onsite classroom. 2+ years in technical training, data science/analytics education, solutions engineering, or similar instructor roles. Broad technical range across AI, Python/SQL/R and Power BI/Tableau; familiarity with Databricks/Snowflake is a plus. Learns new tools quickly; enjoys tailoring content to different personas (analyst, engineer, business user, exec). Strong communicator who can simplify complex topics for technical and non-technical audiences. Detail-oriented and process-driven; delivers sessions and materials with substance. Flexible schedule to support time zones (this is not a 9 to 5); occasional travel for onsite delivery. Some of your key metrics will be: Customer satisfaction: survey, engagement, repeat bookings Profe

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. Making data-driven decisions is key to Plaid's culture. To support that, we need to scale our data systems while maintaining correct and complete data. We provide golden datasets and tooling to teams across engineering, product, and business and help them explore our data quickly and safely to get the data insights they need, which ultimately helps Plaid serve our customers more effectively. In addition, Plaid will not be successful if we can't move quickly. We build the data systems and tools that enable everyone at Plaid to be data-driven, making analytics easy, obvious, and proactive across the company. Data Engineers heavily leverage SQL and Python to build data workflows that integrate with our Golang applications. We use tools like DBT, Airflow, Redshift, Atlan, and Retool to orchestrate data pipelines and define workflows. We work with engineers, product managers, business intelligence, data analysts, and many other teams to build Plaid's data strategy and a data-first mindset. You will be in a high impact role that will directly enable business leaders to make faster and more informed business judgements based on the datasets you build. You will have the opportunity to car

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Why this role? We're building the foundational infrastructure that will define how the world thinks about and deploys AI, and we want the sharpest, most curious people to help us do it. As a member of our Analytics & Data Insights team, you'll tackle the kind of problems that don't have textbook answers yet, launch products that didn't exist a year ago, and help enterprises understand what foundational AI actually means for their bottom line. As a Data Engineer, you will: Work directly on new customer experiences built on one of the most advanced AI systems in the world Collaborate daily with researchers and engineers who are some of the best in the world at what they do Run implementations end-to-end and see initiatives through to real outcomes Partner across research, marketing, sales, and finance to help define how Cohere grows, with your recommendations feeding directly into products and strategy You may be a good fit if you have: 5+ years of experience working on production-grade data processing systems Strong command of Python and SQL Experience with distributed data processing frameworks such as Apache Beam, Spark, or

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Why this role? We're building the data infrastructure behind some of the most demanding AI training workloads in the world, and we want sharp, curious people to help us do it. In this role, you'll build and maintain the high-performance data layer our Modeling teams rely on for training and evaluation jobs. As a Software Engineer, Data Infrastructure, you will: Work directly on petabyte-scale storage infrastructure, and the networking and performance challenges that come with it. Collaborate daily with researchers and engineers who are some of the best in the world at what they do. You may be a good fit if you have: 4+ years of experience working on data storage infrastructure Strong command of Python Kubernetes experience, especially on the storage side (Persistent Volumes, CSI drivers, etc.) The ability to transform unstructured data into performant datasets across diverse storage backends including S3, GCS, and POSIX Experience with distributed data processing frameworks such as Apache Beam, Spark, or Flink [Nice-to-have] Familiarity with modern analytics tooling such as BigQuery, Airflow, or dbt Genuine excitement about AI.

We are investing in agentic AI and need a Senior AI Engineer to lead the design and delivery of these systems. This is a foundational hire: you will own both the agent-facing workstreams — pipelines, orchestration, conversational interfaces — and the underlying context layer that makes them reliable, including memory management, knowledge graph integration, and retrieval infrastructure. You will work closely with data engineers, project leads, and client stakeholders, and play a key role in shaping how Lynx builds and ships AI solutions at scale. What This Involves: Lead the architecture and delivery of agentic AI systems end-to-end: agents, orchestration, tool use, and multi-step reasoning workflows. Own the context layer: design and implement memory architectures (episodic, semantic, working memory) and integrate GraphRAG and knowledge graph retrieval into agentic pipelines. Build robust RAG systems — including vector retrieval, graph traversal, and hybrid search — and ensure retrieval quality through evaluation frameworks. Translate client requirements into technical designs, presenting approaches and trade-offs to both technical and non-technical stakeholders. Define standards and reusable patterns for agentic AI development that other engineers at Lynx can build on. Set up observability, evaluation, and monitoring pipelines to ensure AI systems perform correctly in production. Requirements: 5–8 years of software or ML engineering experience, with at least 2–3 years building LLM-based or agentic AI systems in production. Deep hands-on experience with agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI, or similar) and LLM APIs (OpenAI, Anthropic, etc.). Strong understanding of agent design patterns: ReAct, planning loops, tool use, multi-agent coordination, and memory architectures. Practical experience with GraphRAG or knowledge graph-based retrieval (e.g., Neo4j, Microsoft GraphRAG) and vector databases (Pinecone, Weaviate, Qdrant, etc.). Proficiency in