Cloud Platform Engineering Jobs

P-1327 At Databricks, we are passionate about helping data teams solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers, Databricks has started a multi-year journey to build the best Lakehouse Platform. For this we are building on top of a great foundation but our goal is to go further to build dramatically better products. We want to revisit every component to provide our customers with the fastest, easiest to use and most secure data platform for all their data workloads. As a software engineer, you will join as a founding member of not only the Berlin site, but really as a founding team for our multi-year journey to achieve our Lakehouse vision. You will be involved in the entire development cycle and exemplify all core Databricks values. The impact you'll have: Our backend teams span many domains across our essential service platforms. For instance, you might work on challenges such as: Problems that span from product to infrastructure including: distributed systems, at-scale service architecture and monitoring, workflow orchestration, and developer experience. Build reliable, secure and high performance services and client libraries for storing and accessing humongous amount of data on cloud storage backends, e.g., AWS S3, GCS, Azure Blob Store. Develop product features that empower our customers to easily store and access their data. What we look for: BS (or higher) in Computer Science, or a related field 5+ years of production level experience in one of: Java, Scala, C++, or similar language. Experience developing large-scale distributed systems. Experience working on a SaaS platform or with Service-Oriented Architectures. Knowledge of SQL. About Databricks Databricks is the Data and AI co

P-1346 At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. Databricks Mosaic AI offers a unique data-centric approach to building enterprise-quality, Machine Learning and Generative AI solutions, enabling organizations to securely and cost-effectively own and host ML and Generative AI models, augmented or trained with their enterprise data. And we're only getting started in Bengaluru , India - and currently in the process of setting up 14 new teams from scratch ! As a Staff Software Engineer at Databricks India, you can get to work across : - Backend (Infrastructure) The impact you'll have: Our Infrastructure Backend teams span many domains across our essential service platforms. For instance, you might work on challenges such as: Problems that span from product to infrastructure including: distributed systems, at-scale service architecture and monitoring, workflow orchestration, and developer experience. Deliver reliable and high performance services and client libraries for storing and accessing humongous amount of data on cloud storage backends, e.g., AWS S3, Azure Blob Store. Build reliable, scalable services, e.g. Scala, Kubernetes, and data pipelines, e.g. Apache Spark™, Databricks, to power the pricing infrastructure that serves millions of cluster-hours per day and develop product features that empower customers to easily view and control platform usage. What we look for

P-926 At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer-obsessed — we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. We're only getting started in Bengaluru, India - and are currently in the process of setting up 10 new teams from scratch ! Our engineering teams build technical products that fulfill real, important needs in the world. We always push the boundaries of data and AI technology, while simultaneously operating with the security and scale that is important to making customers successful on our platform. We develop and operate one of the largest-scale software platforms. The fleet consists of millions of virtual machines, generating terabytes of logs and processing exabytes of data per day. At our scale, we observe cloud hardware, network, and operating system faults, and our software must gracefully shield our customers from any of the above. As a software engineer in the Observability team , you will develop observability solutions that provide insights into the health and performance of our products and infrastructure. The impact you'll have: Establish standards for logging, metrics, and tracing. You will collaborate with different teams to identify metrics that allow engineers to observe how well the system and different subcomponents are performing. You will build tooling and infrastructure to allow components to efficiently emit, aggregate, and store metrics that can be displayed on dashboards and used for alerting. Ensure the scalability

Figma is growing our team of passionate creatives and builders on a mission to make design accessible to all. Figma’s platform helps teams bring ideas to life—whether you're brainstorming, creating a prototype, translating designs into code, or iterating with AI. From idea to product, Figma empowers teams to streamline workflows, move faster, and work together in real time from anywhere in the world. If you're excited to shape the future of design and collaboration, join us! We are looking for an experienced Data Engineer to partner with our Data Science and Data Infrastructure teams to own and scale our data pipelines. You’ll also work closely with stakeholders across business teams including sales, marketing, and finance to ensure that the data they need arrives promptly and reliably. You’ll play an integral role in building the metrics and self-serve reporting capabilities to unlock Figma’s next phase of growth. This is a great role for an individual who is passionate about working with data and data systems, and who love s solving problems. You’ll have a good sense for when it makes sense to build fast, scrappy solution s to unblock a key stakeholder vs. when to push back or bring in an outside service. The ideal candidate will be a great communicator who can help coordinate across multiple internal and external teams and take s pride in building end-to-end projects. This is a full time role that can be held from one of our US hubs or remotely in the United States. What you'll do at Figma : Own, build, and maintai n scalable data pipelines that connect various cloud data sources. Develop a deep understanding of Figma’s core data model s and optimize data pipelines for scale. Partner with the Data Science and Data Infrastructure teams to build new foundational data sets that are trusted, well understood, and enable self-service . Work with a wide range of cross-functional stakeholders to derive requirements and architect shared datasets; ability to documen

About the Team At Trendyol Tech, our mission is to create a positive impact in our ecosystem by enabling commerce through technology. We solve complex problems with data, creativity, and agility — always driven by real outcomes. With a culture built on learning, collaboration, and ownership, we grow together while building what’s next. About the Role This role is part of the Security Architecture & Engineering team and focuses on embedding security into systems, platforms, products, and infrastructure by design. The role provides security architecture consultancy to technical and business stakeholders, helping teams make pragmatic, risk-based security decisions while supporting business objectives. It is responsible for identifying architectural security risks, design flaws, and security gaps across new initiatives, infrastructure changes, cloud adoption, platform services, and business-critical systems. In addition, the role contributes to building reusable reference security architectures, design principles, and assessment frameworks aligned with regulatory and industry frameworks such as NIST, ISO, PCI DSS, GDPR, and KVKK. The role also drives process improvement and automation initiatives to improve the scalability, consistency, and maturity of security architecture practices across the organization.

P-988 At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started. Modern data analysis employs sophisticated methods such as machine learning that go well beyond the roll-up and drill-down capabilities of traditional SQL query engines. As a software engineer on the Runtime team at Databricks, you will be building the next generation distributed data storage and processing systems that can outperform specialized SQL query engines in relational query performance, yet provide the expressiveness and programming abstractions to support diverse workloads ranging from ETL to data science. Below are some example projects: Apache Spark™ : Develop the de facto open source standard framework for big data. Data Plane Storage : Provide reliable and high performance services and client libraries for storing and accessing humongous amount of data on cloud storage backends, e.g., AWS S3, Azure Blob Store. Delta Lake : A storage management system that combines the scale and cost-efficiency of data lakes, the performance and reliability of a data warehouse, and the low latency of streaming. Its higher level abstractions and guarantees, including ACID transactions and time travel, drastically simplify the complexity of real-world data engineering architecture. Delta Pipelines : It's difficult to manage even a single data engineering pipeline. The goal of the Delta Pipelines project is to make it simple and pos

P-987 At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started. Modern data analysis employs sophisticated methods such as machine learning that go well beyond the roll-up and drill-down capabilities of traditional SQL query engines. As a software engineer on the Runtime team at Databricks, you will be building the next generation distributed data storage and processing systems that can outperform specialized SQL query engines in relational query performance, yet provide the expressiveness and programming abstractions to support diverse workloads ranging from ETL to data science. Below are some example projects: Apache Spark™ : Develop the de facto open source standard framework for big data. Data Plane Storage : Provide reliable and high performance services and client libraries for storing and accessing humongous amount of data on cloud storage backends, e.g., AWS S3, Azure Blob Store. Delta Lake : A storage management system that combines the scale and cost-efficiency of data lakes, the performance and reliability of a data warehouse, and the low latency of streaming. Its higher level abstractions and guarantees, including ACID transactions and time travel, drastically simplify the complexity of real-world data engineering architecture. Delta Pipelines : It's difficult to manage even a single data engineering pipeline. The goal of the Delta Pipelines project is to make it simple and pos

About Dot Collective We are a new generation consultancy based across UK and EU and founded on the premises of the engineering excellence and empowering people to make an impact. We work with all modern tech stacks and typically run agile scrum on all our projects. About you Are you passionate about data and its transformational powers? Do you like being able to make a huge difference in a limited period of time? We might be just the right place for you. Your key skills and capabilities: · Implementing cloud-native data platforms · Engineering scalable and reliable pipelines · Good knowledge of distributed computing with Spark · Understanding of cloud architecture principles and best practices · Hands-on experience in designing, deploying, and managing cloud resources · Excellent python and SQL skills · Agile ways of working · Experience in cloud automation and orchestration using tools such as CloudFormation or Terraform · Monitoring and performance tuning of cloud-based applications and services Nice to haves: (MLOps): Model Deployment & Serving – Deploy and manage ML models using MLflow, Azure ML, SageMaker, or similar, ensuring scalability and performance. Monitoring & Retraining – Set up model drift detection, performance monitoring, and automated retraining ML Pipelines & CI/CD – Automate end-to-end ML workflows We expect you to have some knowledge about how to architect, design, develop, deploy, and operate a data platform. Our promise to you We will always see you as a human being and will do our very best to support your needs and wellbeing – well-designed co-working and collaboration spaces, remote working patterns that work for you, parenting leave, sabbaticals

About Dot Collective We are a new generation consultancy based across UK and EU and founded on the premises of the engineering excellence and empowering people to make an impact. We work with all modern tech stacks and typically run agile scrum on all our projects. About you Are you passionate about data and its transformational powers? Do you like being able to make a huge difference in a limited period of time? We might be just the right place for you. Your key skills and capabilities: · Implementing cloud-native data platforms · Engineering scalable and reliable pipelines · Good knowledge of distributed computing with Spark · Understanding of cloud architecture principles and best practices · Hands-on experience in designing, deploying, and managing cloud resources · Excellent python and SQL skills · Agile ways of working · Experience in cloud automation and orchestration using tools such as CloudFormation or Terraform · Monitoring and performance tuning of cloud-based applications and services Nice to haves: (MLOps): Model Deployment & Serving – Deploy and manage ML models using MLflow, Azure ML, SageMaker, or similar, ensuring scalability and performance. Monitoring & Retraining – Set up model drift detection, performance monitoring, and automated retraining ML Pipelines & CI/CD – Automate end-to-end ML workflows We expect you to have some knowledge about how to architect, design, develop, deploy, and operate a data platform. Our promise to you We will always see you as a human being and will do our very best to support your needs and wellbeing – well-designed co-working and collaboration spaces, remote working patterns that work for you, parenting leave, sabbatical

P-59 At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started. Modern data analysis employs sophisticated methods such as machine learning that go well beyond the roll-up and drill-down capabilities of traditional SQL query engines. As a software engineer on the Runtime team at Databricks, you will be building the next generation distributed data storage and processing systems that can outperform specialized SQL query engines in relational query performance, yet provide the expressiveness and programming abstractions to support diverse workloads ranging from ETL to data science. Below are some example projects: Apache Spark™ : Develop the de facto open source standard framework for big data. Data Plane Storage : Provide reliable and high performance services and client libraries for storing and accessing humongous amount of data on cloud storage backends, e.g., AWS S3, Azure Blob Store. Delta Lake : A storage management system that combines the scale and cost-efficiency of data lakes, the performance and reliability of a data warehouse, and the low latency of streaming. Its higher level abstractions and guarantees, including ACID transactions and time travel, drastically simplify the complexity of real-world data engineering architecture. Delta Pipelines : It's difficult to manage even a single data engineering pipeline. The goal of the Delta Pipelines project is to make it simple and poss

P-926 At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started in Bengaluru , India ! As a software engineer with a backend focus, you will work with your team to build infrastructure for the Databricks platform at scale. The impact you'll have: Our backend teams cover a diverse range of domains, from core compute fabric resource management to service platforms and infrastructure. For example, you might work on challenges such as: Supporting Databricks' growth by building foundational infrastructure platforms that enable seamless operation across numerous geographic regions and cloud providers. Implementing cloud-agnostic infrastructure abstractions to help Databricks engineers more efficiently manage and operate their services. Develop tools and processes that drive engineering efficiency at Databricks. We enhance the developer experience for Databricks engineers across various areas, including programming languages, linters, static analysis, IDEs, remote development environments, automated release pipelines, and test automation frameworks. Our current focus is on optimizing the Rust development experience across the organization. What we look for: BS (or higher) in Computer Science, or a related field 6+ years of production level experience in one of: Python, Java, Scala, C++, or similar language. Experience developing large-scale distributed systems from scratch Experience working

P-186 At Databricks, we are obsessed with enabling data teams to solve the world's toughest problems, from security threat detection to cancer drug development. We do this by building and running the world's best data and AI infrastructure platform, so our customers can focus on the high value challenges that are central to their own missions. Founded in 2013 by the original creators of Apache Spark™, Databricks has grown from a tiny corner office in Berkeley, California to a global organization with over 1000 employees. Thousands of organizations, from small to Fortune 100, trust Databricks with their mission-critical workloads, making us one of the fastest growing SaaS companies in the world. Our engineering teams build highly technical products that fulfill real, important needs in the world. We constantly push the boundaries of data and AI technology, while simultaneously operating with the resilience, security and scale that is critical to making customers successful on our platform. We develop and operate one of the largest scale software platforms. The fleet consists of millions of virtual machines, generating terabytes of logs and processing exabytes of data per day. At our scale, we regularly observe cloud hardware, network, and operating system faults, and our software must gracefully shield our customers from any of the above. Modern data analysis employs sophisticated methods such as machine learning that go well beyond the roll-up and drill-down capabilities of traditional SQL query engines. As a software engineer on the Runtime team at Databricks, you will be building the next generation distributed data storage and processing systems that can outperform specialized SQL query engines in relational query performance, yet provide the expressiveness and programming abstractions to support diverse workloads ranging from ETL to data science. Below are some example projects: Apache Spark™ : Develop the de facto open source standard framework for big data. Da

P-186 At Databricks, we are obsessed with enabling data teams to solve the world's toughest problems, from security threat detection to cancer drug development. We do this by building and running the world's best data and AI infrastructure platform, so our customers can focus on the high value challenges that are central to their own missions. Founded in 2013 by the original creators of Apache Spark™, Databricks has grown from a tiny corner office in Berkeley, California to a global organization with over 1000 employees. Thousands of organizations, from small to Fortune 100, trust Databricks with their mission-critical workloads, making us one of the fastest growing SaaS companies in the world. Our engineering teams build highly technical products that fulfill real, important needs in the world. We constantly push the boundaries of data and AI technology, while simultaneously operating with the resilience, security and scale that is critical to making customers successful on our platform. We develop and operate one of the largest scale software platforms. The fleet consists of millions of virtual machines, generating terabytes of logs and processing exabytes of data per day. At our scale, we regularly observe cloud hardware, network, and operating system faults, and our software must gracefully shield our customers from any of the above. Modern data analysis employs sophisticated methods such as machine learning that go well beyond the roll-up and drill-down capabilities of traditional SQL query engines. As a software engineer on the Runtime team at Databricks, you will be building the next generation distributed data storage and processing systems that can outperform specialized SQL query engines in relational query performance, yet provide the expressiveness and programming abstractions to support diverse workloads ranging from ETL to data science. Below are some example projects: Apache Spark™ : Develop the de facto open source standard framework for big data. Da

P-150 At Databricks, we are obsessed with enabling data teams to solve the world’s toughest problems, from security threat detection to cancer drug development. We do this by building and running the world’s best data and AI infrastructure platform, so our customers can focus on the high value challenges that are central to their own missions. Founded in 2013 by the original creators of Apache Spark, Databricks has grown from a tiny corner office in Berkeley, California to a global organization with over 1000 employees. Thousands of organizations, from small to Fortune 100, trust Databricks with their mission-critical workloads, making us one of the fastest growing SaaS companies in the world. Our engineering teams build highly technical products that fulfill real, important needs in the world. We constantly push the boundaries of data and AI technology, while simultaneously operating with the resilience, security and scale that is critical to making customers successful on our platform. We develop and operate one of the largest scale software platforms. The fleet consists of millions of virtual machines, generating terabytes of logs and processing exabytes of data per day. At our scale, we regularly observe cloud hardware, network, and operating system faults, and our software must gracefully shield our customers from any of the above. As a software engineer with a backend focus, you will work closely with your team and product management to prioritize, design, implement, test, and operate micro-services for the Databricks platform and product. This implies, among others, writing software in Scala/Java, building data pipelines (Apache Spark, Apache Kafka), integrating with third-party applications, and interacting with cloud APIs (AWS, Azure, CloudFormation, Terraform). Below are some example teams you can join: Data Science and Machine Learning Infrastructure: Build services and infrastructure at the intersection of machine learning and distributed systems. Our te

P-150 At Databricks, we are obsessed with enabling data teams to solve the world’s toughest problems, from security threat detection to cancer drug development. We do this by building and running the world’s best data and AI infrastructure platform, so our customers can focus on the high value challenges that are central to their own missions. Founded in 2013 by the original creators of Apache Spark™, Databricks has grown from a tiny corner office in Berkeley, California to a global organization with over 1000 employees. Thousands of organizations, from small to Fortune 100, trust Databricks with their mission-critical workloads, making us one of the fastest growing SaaS companies in the world. Our engineering teams build highly technical products that fulfill real, important needs in the world. We constantly push the boundaries of data and AI technology, while simultaneously operating with the resilience, security and scale that is critical to making customers successful on our platform. We develop and operate one of the largest scale software platforms. The fleet consists of millions of virtual machines, generating terabytes of logs and processing exabytes of data per day. At our scale, we regularly observe cloud hardware, network, and operating system faults, and our software must gracefully shield our customers from any of the above. As a software engineer with a backend focus, you will work closely with your team and product management to prioritize, design, implement, test, and operate micro-services for the Databricks platform and product. This implies, among others, writing software in Scala/Java, building data pipelines (Apache Spark™, Apache Kafka), integrating with third-party applications, and interacting with cloud APIs (AWS, Azure, CloudFormation, Terraform). Below are some example teams you can join: Data Science and Machine Learning Infrastructure: Build services and infrastructure at the intersection of machine learning and distributed systems. Our