Data Platform Engineer

Secure Every Identity, from AI to Human Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence. This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk. About Okta At Okta, our mission is to enable any organization to use any technology, making the world a more secure and connected place. We are the leading independent provider of identity for the enterprise. We work with an incredible array of customers, from the largest enterprises to the most innovative startups, to help them securely connect their people to technology. As we enter a new phase of growth, our AI and data capabilities will be a critical pillar of our success, powering secure and scalable products that serve both our customers and employees. The Opportunity Okta is seeking a Principal Data Engineer to be the technical visionary for our internal data platform that supports our internal AI use cases with AI ready data. Reporting to the Sr Manager of Enterprise Data Platform, this role is responsible for turning the data product vision into a secure, scalable, and elegant technical reality. You will own the "how"—the technical strategy, implementation, and scalability that brings our AI and data product roadmap to life. This role requires a technical leader who can balance today’s execution with building a durable foundation for the future. You will own the technical standards for engineering excellence, security, and governance, ensuring our core data platform is trusted and ready to power the next generation of AI-powered products and decision-making at Okta. What You’ll Do Own the technical strategy and implementation for the company's AI d

About the Role: We're hiring Senior and Staff Data Platform Engineers to join the Data Infrastructure teams in Toronto. Together these teams own the infrastructure that processes billions of events per day: Spark-on-Kubernetes, Flink and Kinesis pipelines, a multi-petabyte Delta Lake, a large-scale MemoryDB feature store, Databricks multi-environment operations, and the catalog and lifecycle systems that govern it. The team is small and senior. Each engineer owns major platform components: you design it, build it, and support it in production. This is a hybrid-role based out of our Toronto office. You must be willing to travel to our Toronto office two days/week. What You'll Do: Spark-on-Kubernetes — EKS-based compute platform for Spark workloads: cluster configuration, Pod Identity IAM, job environment setup, Kustomize overlays, and shadow canary validation Event ingestion — Rust services and Flink jobs processing billions of events per day over Kinesis; throughput, reliability, on-call response, and AI-assisted operational tooling to reduce toil Platform infrastructure — Terraform modules for environment provisioning, cross-account AWS IAM, ARC runner infrastructure, and CI/CD for data platform changes Feature store and ML compute — Flink-based real-time feature pipelines feeding a large-scale MemoryDB cluster; GPU capacity governance and Databricks multi-environment operations for ML training workloads Workflow orchestration and CDC — Airflow-based DAG deployment, change data capture pipeline operations, and data quality monitoring Your Background: 3+ years building and operating production data platform infrastructure at the cluster or platform level, across Spark, Flink, Kinesis, Kubernetes, or equivalent Deep experience in at least one of: Spark-on-K8s cluster operations, Rust-based data or systems engineering, Kubernetes platform engineering and IaC, or data catalog and governance tooling Production AWS experience or equivalent: EKS, S3, Kinesis, and mu

About DevRev At DevRev, we're building the future of work with Computer – your AI teammate. Unlike traditional tools, Computer unifies all your data sources, tools, and workflows into a single AI-ready platform, giving employees real-time insights, proactive suggestions, and powerful agentic actions. It extends your existing software with AI-native apps and agents that work alongside your teams and customers – updating workflows, coordinating across teams, and eliminating repetitive work. We call this Team Intelligence: human-AI collaboration that breaks down silos, brings people back together, and frees you to solve bigger problems. Backed by Khosla Ventures and Mayfield with $150M+ raised, DevRev is trusted by global companies across industries. About the role: We are looking for a Senior Data Engineer to help build and evolve the data platform that powers critical business decisions and customer-facing experiences. You will own significant parts of our data architecture that is main powerhouse of DevRev Computer’s memory for accurate and efficient Answers. As a part of data team, you will design and operate scalable data systems, and work closely with Software Engineering, AI Agent teams, Data Science, and Product teams to turn complex data requirements into reliable, high-quality data products. This role is ideal for an experienced engineer who enjoys solving challenging problems involving large-scale data, distributed systems, database architecture, and performance optimization . You will have significant technical ownership and the opportunity to influence the direction of our agentic data platform while helping raise the engineering bar across the team. What you'll do: Own data architecture for large-scale, high-impact projects, making thoughtful tradeoffs across scalability, reliability, performance, maintainability, and operational cost. Design, build, and operate scalable data pipelines and data systems that reliably ingest, transform, store, and se

We're looking for an ML Data & Platform Engineer to own the infrastructure that powers our speech AI models: the pipelines that source and prepare training data, and the platform that trains, evaluates, and serves them in production. Speech AI has a data problem most ML teams don't, and you'll be at the centre of solving it, working as part of our ML team to remove friction across the entire lifecycle and get better models into production faster. This is a broad, cross-functional role suited to someone who enjoys working across the full stack: data infrastructure, distributed systems, and production ML, and who takes ownership of problems end to end rather than waiting to be told what to fix. What you'll do Designing, building, and maintaining scalable data pipelines for ingesting, transforming, validating, and storing large datasets used to train our models Developing and maintaining web scraping and data acquisition solutions to keep training datasets fresh, high-quality, and available at scale Building and operating the infrastructure that lets the ML team deploy and evaluate new models quickly, and that serves models efficiently and reliably in production Optimising infrastructure for both iteration speed and production reliability, including GPU utilisation, job scheduling, and training efficiency Implementing observability (monitoring, logging, alerting) across data pipelines and ML systems to catch issues early and keep things running smoothly Troubleshooting complex issues across distributed systems, spanning data infrastructure, training, and inference Continuously improving our data and MLOps practices, and helping shape the roadmap for how our platform evolves as we scale What you'll need Strong proficiency in Python and SQL, with a solid backend or data engineering foundation Hands-on experience with containerisation and orchestration (Docker, Kubernetes), and working with a major cloud provider Experience building data pipelines and ETL/ELT processe

About Hexnode Hexnode is a global leader in Unified Endpoint Management (UEM), trusted in over 100 countries and managing millions of devices worldwide. With continuous innovation across Apple, Windows, Android, macOS, Linux, and tvOS ecosystems, Hexnode is redefining enterprise mobility and cybersecurity. As we expand our Extended Detection and Response (XDR) capabilities, we are building a next-generation data platform designed for massive scale, real-time intelligence, and high-performance security analytics. The Role We are looking for a Senior Data Platform Engineer to architect, scale, and optimize the core data infrastructure powering our XDR product. In this role, you will take ownership of the strategic evolution of our data layer — ensuring seamless ingestion, storage, indexing, search, and retrieval of high-volume security telemetry and device data. You will play a key role in transforming our platform into a highly scalable, cost-efficient, secure, and analytics-ready ecosystem that supports real-time detection, investigation, and advanced data science workflows. This is a high-impact individual contributor role focused on architecture, platform modernization, and long-term scalability. Key Responsibilities Architect and evolve the data layer to support high-throughput ingestion, real-time processing, and low-latency search across massive datasets. Evaluate existing search and storage platforms and drive modernization initiatives toward more scalable, performant, and cost-effective solutions. Design and implement optimized indexing, partitioning, and data structuring strategies for efficient querying and retrieval. Build and manage scalable data lake architectures that support advanced analytics, threat intelligence, and data science workflows. Define strategies for lifecycle management, retention, tiered storage, and cost optimization across the data platform. Ensure high availability, fault tolerance, and disaster recovery across distributed systems. I

We're looking for an ML Data & Platform Engineer to own the infrastructure that powers our speech AI models: the pipelines that source and prepare training data, and the platform that trains, evaluates, and serves them in production. Speech AI has a data problem most ML teams don't, and you'll be at the centre of solving it, working as part of our ML team to remove friction across the entire lifecycle and get better models into production faster. This is a broad, cross-functional role suited to someone who enjoys working across the full stack: data infrastructure, distributed systems, and production ML, and who takes ownership of problems end to end rather than waiting to be told what to fix. What you'll do Designing, building, and maintaining scalable data pipelines for ingesting, transforming, validating, and storing large datasets used to train our models Developing and maintaining web scraping and data acquisition solutions to keep training datasets fresh, high-quality, and available at scale Building and operating the infrastructure that lets the ML team deploy and evaluate new models quickly, and that serves models efficiently and reliably in production Optimising infrastructure for both iteration speed and production reliability, including GPU utilisation, job scheduling, and training efficiency Implementing observability (monitoring, logging, alerting) across data pipelines and ML systems to catch issues early and keep things running smoothly Troubleshooting complex issues across distributed systems, spanning data infrastructure, training, and inference Continuously improving our data and MLOps practices, and helping shape the roadmap for how our platform evolves as we scale What you'll need Strong proficiency in Python and SQL, with a solid backend or data engineering foundation Hands-on experience with containerisation and orchestration (Docker, Kubernetes), and working with a major cloud provider Experience building data pipelines and ETL/ELT processe