Senior Data Reliability Engineer Jobs

About the Role Amplitude's Cloud Platform team builds the systems that every Amplitude engineer relies on every day to ship code — and we're rebuilding them for the AI era. As a Senior Platform Engineer, you'll own medium-to-high-complexity platform projects end-to-end and help shape a platform where AI agents are first-class users alongside humans: kicking off deploys, opening pull requests against infrastructure, and triaging incidents, so a single engineer can get the throughput of a team. You'll partner with Staff engineers and product teams to make Kubernetes effortless across the engineering org, building self-service automation and scalable AWS infrastructure that lets product teams ship faster, safer, and with less cognitive load. If you're excited about building the systems that other engineers will rely on every day, this role is for you. Key Responsibilities Lead high-impact platform projects — design and ship capabilities that move the needle on developer experience, reliability, or security, and set the bar for quality, testing, and safe deployment practices. Build the AI-augmented platform. Design tooling and workflows that help engineers get more out of AI-assisted development — think infra primitives that are easy to reason about, automated review, and policy-as-code that keeps the guardrails strong as AI shifts how code gets written. Own Infrastructure-as-Code for Kubernetes, AWS, and GCP using Terraform, Helm, Kustomize, and emerging tooling — and make it consumable enough that an LLM can safely PR against it. Evolve our CI/CD backbone (Argo CD / Workflows / Rollouts, GitHub Actions) to make deploys faster, safer, and easier to reason about. Instrument and operate. Drive observability with Datadog and Amplitude, own dashboards and SLOs, and use the data to push reliability forward. Participate in on-call, lead incident response when needed, and turn postmortems into durable platform improvements. Reduce toil and tech debt with pragmatic remediation

SpyCloud is on a mission to make the internet a safer place by disrupting the criminal underground. SpyCloud’s solutions thwart cyberattacks and protect more than 4 billion accounts worldwide. Cybersecurity is an exciting, evolving space, and being at the forefront of the fight to disrupt cybercrime makes SpyCloud a special place to work. If you’re driven to align your career with a fantastic mission, look no further! This Data Engineer II role sits on the Engineering team, reporting to the Senior Director of Data Engineering. Your focus will include evolving our data platform using technologies such as Databricks and Spark, building scalable data pipelines, and serving customers through purpose-built data stores including RDS, Elasticsearch, and DynamoDB. This is a hands-on individual contributor role with ownership of complex data engineering initiatives that support the scalability, reliability, and continued evolution of SpyCloud’s data platform. What You'll Do: Design, build, and improve scalable data systems with a focus on performance, and reliability. Use Databricks and Spark to prepare, transform, and serve high-quality data to customers and downstream systems. Build, optimize, and maintain real-time and batch data pipelines using AWS technologies such as Lambda, S3, DynamoDB, ECS, EKS, and Terraform, as well as develop and maintain pipelines within the Databricks ecosystem. Identify and implement improvements to the cost and performance of data ingestion, transformation, and storage workflows. Be available for on-call support to troubleshoot and remediate data pipeline failures outside of standard business hours. Partner with Product Management, SpyCloud Labs, and Engineering teams to understand requirements and develop effective technical solutions. Evaluate and implement modern tooling for data processing, ML/AI data workflows, and observability. Contribute to architectural and technical design decisions and help implement scalable solutions align

About the Team The Office Technology team designs, deploys, and operates the network infrastructure that keeps Datadog’s global offices connected, secure, and reliable. The team owns routing, switching, wireless, office firewalls, connectivity, and the observability and operational practices needed to provide a dependable employee experience at scale. About the Role As a Senior Network Engineer on the Office Technology team, you will own major network services and complex multi-site initiatives from problem definition and design through implementation, migration, and stable operations. You will act as a technical anchor for the team, leading design reviews and difficult troubleshooting, improving standards and automation, and helping other engineers deliver high-quality work. This is a senior level role that combines hands-on engineering with technical leadership. You will shape team roadmap proposals and technical decisions through evidence and clear tradeoffs, while remaining accountable for reliable delivery and day-to-day operability. What You’ll Do Design resilient, scalable solutions across LAN , Wi-Fi, routing, switching, and office firewalls, with explicit failure domains, capacity assumptions, security controls, and migration paths Author RFCs and lead design reviews, clearly documenting goals, constraints, alternatives, decisions, risks, validation, rollout, and rollback Build production readiness into network changes through meaningful service objectives, monitoring, alerting, runbooks, capacity planning, failure testing, and operational handoff Design reusable automation for provisioning, configuration, policy, compliance, telemetry, and change validation, with appropriate testing, peer review, drift detection, staged rollout, and safe rollback Own, alongside other senior engineers and leadership, the health and lifecycle of major network services, using data to improve reliability, coverage, performance, security, supportability, and cost Lead di

About the Role Chime’s Data Platform team builds the infrastructure every engineering and analytics team depends on - ingestion, transformation, quality, governance, and self-serve tooling across batch and streaming workloads. You’ll own core platform systems at scale, design the frameworks other teams build on, and set the technical standards for how data moves through Chime. High autonomy, real architectural trade-offs, production impact from day one. We’re hiring multiple engineers into this role at the senior level. In this role, you can expect to Design, build, and operate self-serve ETL/ELT frameworks supporting both batch and streaming workloads, with direct ownership of pipeline reliability, data quality SLAs, and schema evolution Drive the technical roadmap for our data platform - evaluate build-vs-buy decisions, define integration patterns, and set standards that engineering teams across the company adopt Partner with Product Engineering, Data Science, Analytics, and Marketing as a technical lead to design data contracts, onboard new data domains, and scale ingestion without sacrificing governance Architect and enforce data lineage, schema registry, and access controls across all domains - ensuring compliance with fintech regulatory requirements (SOX, PII handling) Own the platform - monitor, alert, debug, and resolve incidents across the data platform with the urgency expected of infrastructure that powers financial products Raise the technical bar of the team through code review, design reviews, and hands-on mentorship of junior and mid-level engineers Engineers are required to participate in on-call rotation. Being on call may include responding to incidents outside of regular working hours when necessary. To thrive in this role, you have 5+ years building, shipping, and operating data infrastructure in production - not just writing pipelines, but owning their reliability, performance, and cost at scale Strong system design skills - you’ve written desig

The NVIDIA DGXC Data Services team builds cloud-native systems, frameworks, and services for managing data across hybrid and multi-cloud infrastructure. We are building the next-generation data and storage infrastructure to solve some of the hardest problems in AI: storage, access, ingestion, governance, observability, and data management for exabyte-scale, high-performance GPU-based training and inference jobs. Our work gives NVIDIA teams the foundational capabilities they need to build, train, deploy, and operate AI products at scale without reinventing critical data infrastructure for every workload. What you will be doing: Build storage technologies, client libraries, and filesystem frameworks that help AI workloads access data across object stores, file systems, and hybrid cloud infrastructure. Develop high-performance storage paths for training and inference workflows, including data loading, checkpointing, caching, POSIX-style access, and object-store integration. Build observability systems that diagnose storage bottlenecks, attribute GPU idle time to I/O behavior, and expose actionable telemetry through production monitoring stacks. Improve performance, scalability, and reliability of storage systems serving massive datasets, deep directory trees, and high-concurrency AI workloads. Work closely with internal AI teams, platform teams, SRE, and operations to validate storage behavior against real workloads and production environments. Use modern software engineering practices, including AI-assisted and agentic development workflows, while maintaining high standards for design, testing, security, performance, and verification. What we need to see: BS in Computer Science, Information Sys

The NVIDIA DGXC Data Services team builds cloud-native systems, frameworks, and services for managing data across hybrid and multi-cloud infrastructure. We are building the next-generation data and storage infrastructure to solve some of the hardest problems in AI: storage, access, ingestion, governance, observability, and data management for exabyte-scale, high-performance GPU-based training and inference jobs. Our work gives NVIDIA teams the foundational capabilities they need to build, train, deploy, and operate AI products at scale without reinventing critical data infrastructure for every workload. What you will be doing: Build cloud-native data and storage services for hybrid and multi-cloud infrastructure, including dataset discovery, ingestion, governance, checkpointing, observability, and low-latency access. Develop scalable cloud-native services and APIs that support exabyte-scale, high-performance GPU training and inference workflows. Work closely with product managers, internal AI teams, platform teams, and partner engineering teams to understand requirements and turn them into reliable production systems. Collaborate with SRE, operations, and support teams to improve service reliability, performance, observability, on-call readiness, and operational scale. Use modern software engineering practices, including AI-assisted and agentic development workflows, while maintaining high standards for design, testing, security, and verification. What we need to see: BS in Computer Science, Information Systems, Computer Engineering, or equivalent experience, with 5+ years of software engineering experience. Strong foundation in algorithms, data structures, distributed systems, and practi

About Forma.ai: Forma.ai is a Series B startup that's revolutionizing how sales compensation is designed, managed and optimized. We handle billions in annual managed commissions for market leaders like Edmentum, Stryker, and Autodesk. Our growth has been fuelled by our passion for fundamentally changing and shaping how companies use sales intelligence to drive business strategy. We’re welcoming equally driven individuals who are excited about creating something big! About the Team Engineers on this team build our rules-based calculation engine for processing sales commissions. This might sound simple if you have never been exposed to sales compensation plans, it is not. We are low on meetings and high on accountability. Most of the team is in the EST time zone, with a few located in PST and Central as well. We are still evolving many areas of the platform, which means there is meaningful room to improve the design, reliability, and scalability of the systems we build. What you’ll be doing Reporting to the Manager of Data Platform, you will play an important role in the evolution of our Spark-based data platform. You’ll design and build data-rich platform capabilities, contribute to system design discussions, and help ensure our data systems remain reliable, maintainable, and scalable as Forma grows. As a Senior Engineer, Data Platform, you are expected to operate with strong ownership and sound technical judgment. This includes identifying risks in the work you own, surfacing edge cases, asking thoughtful questions, and proposing improvements that strengthen the quality and reliability of the platform. You will: Design, build, and improve Spark-based data pipelines and platform services. Work with complex data models representing sales compensation plans, hierarchies, relationships, and enterprise datasets. Build reliable, deterministic data systems that customers and internal teams can trust. Improve testing, observability, data quality,

Senior Software Engineer - Observability and Reliability About the Role We are growing the engineering team and looking for engineers who have the chops to build and deliver world-class technology. You will be part of a talented team of engineers with a shared mission to make data easily accessible. What You Will Be Doing Build observability tools and platforms, including: metrics, logging, distributed tracing, dashboarding, alerting, application performance management Build with modern tools and languages like Go, Open Telemetry and Kubernetes Participate in on-call rotation and ensure uptime of services Create runtime tools/processes that optimize cloud triaging and limit downtime Define best practices around making our systems and services measurable Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies. We expect successful candidates to be coding a majority of their time Qualifications We Need Strong Computer Science fundamentals 5+ years industry experience building and maintaining high-quality software, especially software other engineers use You apply a product mindset to infrastructure systems and feel accomplished enabling others Desire to be a great teammate and have fun at work Strong sense of craftsmanship, and a healthy academic curiosity Qualifications We Want (also, skills you’ll learn!) Experience building systems for data analytics Distributed systems monitoring and profiling skills Knowledge of cloud application security models Administered cloud service infrastructure (GCP, AWS, Azure) Startup experience Additional Job details Additional Job details The base salary range for this position is $170k - $240k annually. Compensation may vary outside of this range depending on a number of factors, including a candidate’s qualifications, skills, competencies and experience. Base pay is one part of the Total Package that is provided to compensate and recognize e

Senior Software Engineer - Observability and Reliability About the Role We are growing the engineering team and looking for engineers who have the chops to build and deliver world-class technology. You will be part of a talented team of engineers with a shared mission to make data easily accessible. What You Will Be Doing Build observability tools and platforms, including: metrics, logging, distributed tracing, dashboarding, alerting, application performance management Build with modern tools and languages like Go, Open Telemetry and Kubernetes Participate in on-call rotation and ensure uptime of services Create runtime tools/processes that optimize cloud triaging and limit downtime Define best practices around making our systems and services measurable Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies. We expect successful candidates to be coding a majority of their time Qualifications We Need Strong Computer Science fundamentals 5+ years industry experience building and maintaining high-quality software, especially software other engineers use You apply a product mindset to infrastructure systems and feel accomplished enabling others Desire to be a great teammate and have fun at work Strong sense of craftsmanship, and a healthy academic curiosity Qualifications We Want (also, skills you’ll learn!) Experience building systems for data analytics Distributed systems monitoring and profiling skills Knowledge of cloud application security models Administered cloud service infrastructure (GCP, AWS, Azure) Startup experience Additional Job details Additional Job details The base salary range for this position is $170k - $240k annually. Compensation may vary outside of this range depending on a number of factors, including a candidate’s qualifications, skills, competencies and experience. Base pay is one part of the Total Package that is provided to compensate and recognize e

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Responsibilities: Provide L2 support in incident, request, change and problem management related to database operations. Provide operational support covering 24/7 and participate in Major incidents. Ensure SLA for incidents and service requests are met with high quality of work. Ensure critical metrics, such as breach case target, MTTR and first level resolution target is met. Install and upgrade database systems, apply patches and implement changes when required. Diagnose and resolve database errors ensuring high availability of database systems. Manage databases access, backups and ensure compliance to database and security standards. Monitor database performance proactively and perform analysis of cases to prevent recurring issues. Review and improve database monitoring when required. Develop processes and operation procedures. Build and manage database reports, visualizations, and dashboards related database operations. Leverage AI and agentic solutions to automate operational workflows, enhance service reliability, and improve IT operations efficiency. Requirements: Bachelor’s, Master’s degree or equivalent experience Computer Science, Electrical & Electronics/Computer/Software Engineering, Information Systems or related fields. Good understanding and hands-on experience in the following areas will be advantageous: Microsoft SQL Server, Oracle Database, Linux and Windows operation sy

Omada Health is on a mission to bend the curve of chronic disease. Job overview: We are seeking a Senior Software Engineer to help design, build, and scale modern data platform capabilities using cloud-native technologies and AI-driven solutions. In this role, you will develop reusable frameworks, platform services, and engineering patterns that enable teams to build reliable, scalable data-products and AI-powered workflows. You will also leverage Generative AI to enhance data discovery, governance, developer productivity, and self-service access to trusted data. Working closely with cross-functional teams, you will help shape a modern data platform that accelerates innovation while ensuring security, reliability, and operational excellence. Your Impact: Design, build, and operate scalable Data & AI platform capabilities for analytics, data science, and Generative AI use cases with strong practices around data quality, lineage, observability, security, and governance. Develop reusable abstractions, templates, services, and workflows that standardize how teams build and manage data pipelines and data products. Partner with Analytics, Applied-AI, Data-engineering and application teams to deliver platform capabilities that support business and clinical decision-making Evaluate and integrate modern data and AI technologies with a focus on scalability, reliability, developer experience, and cost efficiency Apply Generative AI technologies to improve data enablement, documentation, discovery, workflow automation, and platform usability Promote software engineering best practices, including clean code, automated testing, CI/CD, infrastructure-as-code, monitoring, and secure data handling About you: 5+ years of professional software engineering or data engineering experience building scalable data platforms, services, pipelines, or analytics infrastructure Hands-on experience with modern data architectures, including data lakes, lakehouses, warehouses, analytical datast

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. Observe by Snowflake is an AI-powered observability platform built on the Snowflake AI Data Cloud and engineered for scale. We ingest and store logs, metrics, traces, and events on an open, scalable data lakehouse using open formats like Apache Iceberg — at dramatically lower cost. A dynamic Context Graph and chat-based AI SRE provide rich context and automated workflows so teams can move from detection to root cause and resolution 10x faster. Leading engineering teams at companies like Capital One, Topgolf, and Dialpad rely on Observe to troubleshoot hundreds of terabytes of telemetry daily while maintaining reliability at enterprise scale. As part of Snowflake, Observe combines startup-style ownership and velocity with the global reach, operational excellence, and ecosystem of one of the world's leading data platforms. We are hiring a Senior Software Engineer for the Observe Data Management team. This team owns the core pipelines that ingest and process over 1 petabyte of telemetry data per day — the foundational infrastructure powering Observe's entire observability stack. You'll be working at the intersection of massive scale, open-source innovation, and real-world reliability challenges for enterprise customers around the globe. AS A SENIOR SOFTWARE ENGINEER - OBSERVE

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

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-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