Data Analytics Engineer Jobs

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

Product Engineer (Advance Data Analytics), Heterogeneous Integration Group (HIG), High Bandwidth Memory (HBM) Product Engineering — Fab 10A, Singapore. Apply via Workday.

Product Engineer (Advance Data Analytics), Heterogeneous Integration Group (HIG), High Bandwidth Memory (HBM) Product Engineering — Fab 10A, Singapore. Apply via Workday.

NVIDIA has continuously reinvented itself over two decades. Our invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing. NVIDIA is a “learning machine” that constantly evolves by adapting to new opportunities that are hard to solve, that only we can address, and that matter to the world. This is our life’s work, to amplify human creativity and intelligence. Make the choice to join us today. Design-for-Test Engineering at NVIDIA works on groundbreaking innovations involving crafting creative solutions for DFT architecture, verification and post-silicon validation on some of the industry's most complex semiconductor chips. What you'll be doing: As a senior member in our team, you will work with pre-silicon and post-silicon data analytics - visualization, insights and modeling. Design and uphold sturdy data pipelines and ETL processes for the ingestion and processing of DFX Engineering data from various origins Lead engineering efforts by collaborating with cross-functional teams (execution, analytics, data science, product) to define data requirements and ensure data quality and consistency You will work on hard-to-solve problems in the Design For Test space which will involve application of algorithm design, using statistical tools to analyze and interpret complex datasets and explorations using Applied AI methods. In addition, you will help develop and deploy DFT methodologies for our next generation products using Gen AI solutions. You will also help mentor junior engineers on test designs and trade-offs including cost and quality. What we need to see: BSEE (or equivalent experience) with 5+, MSEE with 3+, or PhD wi

Staff Product Engineer (GenAI, AI/ML & Advanced Data Analytics) — Fab 10A, Singapore. Apply via Workday.

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Senior Data Engineer At Snowflake, we are building the future of the data-driven enterprise. We are looking for a Senior Data Engineer who brings deep technical craft, strong ownership instincts, and the ability to operate across the full data lifecycle — from raw ingestion to production-ready data products. This is not a role for someone who executes tickets. You will design and build the data infrastructure that powers internal decision-making and external product capabilities, working at the intersection of data engineering, platform thinking, and stakeholder alignment. You will own complex technical decisions, set the bar for data quality and governance, and contribute to a data platform that scales with the business. The person we are looking for combines engineering rigour with pragmatic judgement — someone who can solve for today while building for the future, and who raises the standard of the teams they work within. AS A SENIOR DATA ENGINEER AT SNOWFLAKE, YOU WILL: Design, build, and launch production-ready data models and pipelines that scale effectively across the enterprise data lifecycle — from ingestion through transformation, modelling, and consumption Own complex system design decisions end-to-end, evaluating tradeoffs and documenting architectural choices c

#Team Nextdoor Nextdoor (NYSE: NXDR) is the essential neighborhood network. Neighbors, public agencies, and businesses use Nextdoor to connect around local information that matters in more than 350,000 neighborhoods across 11 countries. Nextdoor builds innovative technology to foster local community, share important news, and create neighborhood connections at scale. Download the app and join the neighborhood at nextdoor.com . Meet Your Future Neighbors The Analytics Engineering team at Nextdoor transforms diverse data sources into solutions for business challenges. As a lean yet impactful team, we cover the entirety of the Nextdoor business and excel in cross-functional collaboration to empower company-wide data-driven decision-making. By amplifying the voices of our platform's users, we play a pivotal role in building stronger, healthier communities. We’re at an exciting transition, evolving from a traditional Business Intelligence focus to a broader mandate that includes strengthening data foundations and developing data products, such as analytics self-serve, for all Nextdoor data consumers. At Nextdoor, we operate in an AI-first environment and expect every team member to actively use AI tools as part of their workflow. We aren't looking for prompt engineers; we’re looking for people who use tools like Claude, Gemini, ChatGPT, and Glean to challenge their own thinking and take full ownership of AI-assisted outputs. We also offer a warm and inclusive work environment that embraces a hybrid employment model, blending an in office presence and work from home experience for our valued employees. The hiring team will go over these expectations with you if you are being considered for a role near one of our offices in San Francisco, Los Angeles, Chicago, Dallas, New York, and London. The Impact You’ll Make As a Senior Analytics Engineering Manager , you will Develop and own the vision for Analytics Engineering, delivering curated datasets, dashboards, and

Senior Analytics Engineer The Data Science & Analytics team at Asana is how the company turns data into decisions — defining the questions that matter, surfacing the answers, and making sure insight is at the center of every critical product and business call . As a Senior Analytical Engineer, you sit at the intersection of Data Engineering, Analytics, and Data Science, and you own the data foundations for a business domain end to end. Your mandate is to turn raw data into reliable, business-ready datasets that PMs, analysts, data scientists, and leaders actually trust and use — and to define the business logic and metric standards that make AI-powered self-serve trustworthy. You consume governed Silver tables and produce the Gold layer and semantic layer beneath Asana's most important metrics, dashboards, and Genie spaces. This role is based in our Vancouver office with an office-centric hybrid schedule . The standard in-office days are Monday, Tuesday, and Thursday . Most Asanas have the option to work from home on Wednesdays . Working from home on Fridays depends on the type of work you do, and your recruiter can share more about the in-office requirements . What you’ll achieve Own the Gold layer for a given business domain (e.g., PLG funnel, marketing attribution, revenue, NPI/AWM): Design and continuously improve the curated, dimensional data models that downstream dashboards, Genie spaces, and ELT reporting depend on. Implement the canonical business logic behind your domain's core KPIs: Translate KPIs into governed, versioned metric marts that resolve "this number doesn't match" disputes for good. Build and curate the semantic layer and Genie spaces that power self-serve in your domain: Author the metadata, documentation, and prompt/metric definitions that let stakeholders query governed data in plain language through Claude and Databricks Genie. Own the metric dictionary for your domain: a single source of truth for what each metric means,

Ready to do the most impactful work of your career? At Coinbase , we are uncompromising on our mission to increase economic freedom. The bar is high, the environment is intense, and we like it that way. This isn't a place for complacency, it’s a place to be pushed past your perceived limits. If you're ready to build the future of finance alongside people who refuse to settle for "good enough," you belong here. Coinbase is a remote-first, but not remote-only company. Expect to get together quarterly for intense in-person working sessions called “surges.” learn more about working at Coinbase . Senior Analytics Engineer As a Senior Analytics Engineer on the Platform team, you'll build the scalable data models and pipelines that power analytics, experimentation, and decision-making across Coinbase. Our Analytics Engineering team transforms raw data into trusted, well-modeled sources that stakeholders across Product, Engineering, and Data Science rely on daily. You'll own end-to-end data solutions for specific business domains, turning complex data flows into clean, reusable frameworks that unlock commercial value at scale. What you'll do: Own end-to-end data modeling for assigned business domains, from understanding source system data flows through designing modular, reusable models (star/snowflake schemas) that serve as the single source of truth for downstream teams. Build and optimize ETL/ELT pipelines using modern tools like dbt and Airflow, ensuring data quality, reliability, and performance at scale across Snowflake or similar warehouse architectures. Partner with Engineering, Product, and Data Science teams to identify data gaps, define requirements, and deliver data products that directly enable experimentation, ad hoc analysis, and business metric optimization. Develop scalable abstractions and frameworks (UDFs, Python packages, internal data apps) that multiply the efficiency of other data teams and reduce time-to-insight across the organization. D

Shape the Future with Dun & Bradstreet At Dun & Bradstreet, we believe data has the power to create a better tomorrow. As a global leader in business decisioning data and analytics, we help companies worldwide grow, manage risk, and innovate. Since 1841, businesses have trusted us to turn uncertainty into opportunity. We’re a diverse, global team that values creativity, collaboration, and bold ideas. Are you ready to make an impact and help shape what’s next? Join us! Explore opportunities at dnb.com/careers. The Full Stack Data Engineer is responsible for designing, developing, supporting, and enhancing Trade Operations applications, data pipelines, and cloud-based services that power the end-to-end Trade data lifecycle. This role combines software engineering, data engineering, cloud technologies, and operational support to ensure Trade data is processed accurately, securely, and efficiently from ingestion through product delivery. The engineer will partner closely with Trade Operations Analysts and Technology teams to build scalable solutions, automate manual processes, improve data quality, modernize legacy platforms, and support strategic initiatives such as market onboarding, cloud migration, and AI-driven process improvements.

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

About the Role As a member of the Data team within the Go-to-Market organization, you will help build a data-driven culture, improve decision-making, and advance strategic initiatives through analytics. This is a full-stack data role spanning data modeling, metric definition, visualization, analysis, and self-service tooling. You will build trusted, scalable data sources and products that give the business reliable, actionable insights. The work calls for judgment: you will choose the tool, approach, and level of investment that best fit each problem, from a focused analysis to a durable production data product. As a core partner to the GTM organization, you will address both foundational and ad hoc analytics needs. You will turn complex data into clear narratives that help technical and non-technical audiences understand what is happening, why it matters, and what they should do next. In This Role, You Will Partner closely with GTM teams to proactively identify high-impact questions and translate business needs into data models, metrics, analyses, and scalable technical solutions. Define, source, validate, and operationalize the metrics that guide the business, helping teams incorporate them into planning and day-to-day decisions. Lead cross-functional data projects across established and emerging business areas, including setting the data strategy for greenfield domains. Build scalable data models and pipelines that integrate and transform data from multiple sources into trusted, accessible datasets. Create dashboards, reports, analytical tools, and other data products that enable stakeholders to answer questions independently. Own the lifecycle of metrics, analytical models, and data products from initial exploration and prototyping through production and ongoing maintenance. Choose the most effective approach for each problem—whether an analysis, metric, data model, visualization, or self-service product—based on the audience, urgency, complexity, and expected v

Our mission and customers: We are creating the freedom for SMEs to succeed by delivering Europe's leading finance workspace with banking at its core, augmented by financial tools. We are proud to be rated 4.8 on Trustpilot, based on 55,000+ reviews. Our culture puts customer satisfaction at the core of what we do, as proven by our Net Promoter Score of 75 (more about our culture here). Our journey: Founded in 2017 by Alexandre and Steve, Qonto has grown to 1,600+ Qontoers serving over 600,000+ customers across 8 European countries. We have been profitable since 2023, and we are just getting started. Our beliefs: We hire for skills and potential. With 80+ nationalities, 45% women, of which 56% of women in our leadership team, diversity isn't a program; It's who we are. We've built a discrimination-free hiring process because the best teams are built on merit. AI at Qonto: AI is deeply embedded in how we work (here) - Every Qontoer gets unlimited access to the best AI tools. We want people who experiment without waiting for permission, push AI beyond the obvious, know when to trust it, and when to question it. ------------------------------------------------------------------------------------------------------ ➡️ Mission: Join us as Analytics Engineer x Business Analytics and become the person our Business Analytics teams can build on without a second thought. You will own end-to-end the data models feeding Product, Growth, and Ops Finance analytics — designing scalable dbt models, pushing back on requests that would trade reliability for speed, and helping the team migrate to Omni and a real semantic layer. You will work closely with Jules Jeanroy, our Analytics Engineering Manager, and partner daily with Business Analysts across Product, Growth, and Ops Finance. The team is at a pivotal moment — investing in scalability, cutting technical debt, and building the standards that will define how Analytics Engineering works at Qonto for years to come. ➡️ As an Analyti

Everpure (NYSE: P) has evolved from storage pioneer to data platform, closing fiscal 2026 with $3.7 billion in revenue, its first billion-dollar quarter, and accelerating growth into FY27. Our strategic agenda spans the companies defining the next era of technology - hyperscalers, AI labs, the AI hardware supply chain, data platform providers, and the broader AI ecosystem. This type of work—work that changes the world—is what the tech industry was founded on. So, if you're ready to seize the endless opportunities and leave your mark, come join us. THE ROLE SHOULD YOU ACCEPT THIS CHALLENGE… The GTM BI team at Pure Storage supports Revenue Operations across pipeline, forecasting, seller performance, and executive reporting — and we are at an inflection point. We are midway through a strategic migration to dbt as our transformation standard. Beyond that, our roadmap includes scalable automation frameworks, semantic business intelligence, and AI-enabled workflows that help surface insights with less manual effort. As a Senior Analytics Engineer, you will help drive that journey end-to-end. This role is grounded in modernization first: building scalable dbt foundations, improving quality and engineering standards, and enabling the team to operate more efficiently before scaling broader AI-enabled capabilities. You will partner closely with BI analysts, Sales Operations, Systems teams, and backend engineering partners to modernize how analytics is engineered, governed, and consumed across the organization. This role is for someone who wants to build — not maintain. WHAT YOU’LL DO… Lead and contribute hands-on to the team’s dbt and Snowflake modernization initiatives Build scalable semantic and metrics layers supporting GTM domains including pipeline, forecasting, quota, attainment, and seller performance Design and maintain robust ELT/ETL pipelines, orchestration workflows, and reusable business-facing datasets Build CI/CD, testing, observability, lineage, and data qualit

At Vanta, our mission is to help businesses earn and prove trust. We believe that security should be monitored and verified continuously, and we empower companies to practice better security and prove it with ease. Vanta has a kind and talented team, and while some have prior security experience, many have been successful at Vanta without it. As a Senior Analytics Engineer, you’ll be responsible for laying the foundation for a best-in-class analytics function. You’ll partner closely with our data science team and business stakeholders to ensure that our analytics stack and processes meet the business needs today with an eye towards the future. What you’ll do as an Senior Analytics Engineer at Vanta: Design and implement complex data models to enable dashboards, self-serve analytics, and data science teams. Write highly tuned, scalable SQL queries running over large-scale, heterogeneous data warehouses. Enable AI tooling with semantic layers and observability, guiding analytics functions on best practices. Manage and improve data infrastructure needed to drive data-driven decision-making solutions. Help develop front end applications to expose analytical data sets enterprise wide Work with the Product and Corporate Engineering system teams to structure source systems for reporting consumption across the enterprise. How to be successful in this role: Have at least four years of experience working with data and two years of experience in Software Engineering or a related field. Have experience with common analytics tooling (e.g. dbt is a must, Stitch/Fivetran, Snowflake/BigQuery/Redshift, Airflow, Dagster, Looker/Mode/Sigma). Bring a system-oriented and software engineering mindset to the Analytics Engineering practice. We’re looking to build frameworks that manage data, and minimize bespoke queries. Deep knowledge of crafting dimensional and fact models in modern data fashion. Have a passion for enabling the developer experience of data, and being obsessed with giving