Role Description We’re looking for a curious, technically strong, and automation-minded Data Platform Engineer to join our Data Engineering team. This is a high-impact platform engineering role for someone who enjoys building reliable systems, writing maintainable automation, and helping data teams move faster, more safely, and with more confidence. You’ll work at the intersection of data engineering, business intelligence, finance analytics, cloud infrastructure, governance, and AI-enabled decision support. What we’re really hiring for is strong engineering fundamentals, comfort with ambiguity, and the drive to apply those fundamentals across different systems. We’re hiring an engineer who can pick up infrastructure work, governance work, or pipeline work as needed and help turn knowledge that currently lives in repos, configs, and people’s heads into durable platform practices. There will be a lot to learn here, even if you arrive experienced. That’s the appeal, not the catch. What You’ll Do - Keep the data platform healthy: - Monitor infrastructure and pipelines, respond to issues, troubleshoot failures from logs and metrics, and help identify root causes so we can prevent repeat problems. - Make infrastructure changes safely: - Build, review, and deploy infrastructure through code using Terraform, AWS, Kubernetes or container-based deployment patterns, and CI/CD workflows. - Improve automation and tooling: - Write Python, bash, and CI/CD automation that reduces manual toil, improves reliability, and makes common platform tasks safer and easier for the team. - Support access control and governance: - Help design and operate role-based access control, least-privilege access, and governance controls across our cloud, warehouse, and BI platforms. - Contribute to data pipelines: - Build, operate, and troubleshoot pipelines and transformations that power reporting, analytics, finance workflows, and data products across the company. - Support our platform migration: - Help move workloads from our legacy stack to the modern data platform, including validation, cutover, and decommissioning work. - Document how things work: - Write clear runbooks, platform documentation, and operational guides so knowledge is easier to share and the team is less dependent on any one person. - Use and improve AI-assisted engineering: - Work with our Claude Code-based tooling, including agents, skills, hooks, and MCP integrations, and help shape how the team uses AI to improve engineering workflows. Qualifications - 3+ years in data engineering, software engineering, DevOps, platform engineering, or a related technical role. - Hands-on experience operating workloads in AWS or another major cloud provider, with a solid grasp of IAM, networking, compute, managed services, deployment patterns, and day-to-day cloud operations. - Proficiency in Python for automation, internal tooling, platform workflows, or data engineering support. - Working knowledge of SQL. Advanced SQL is a plus, but not a prerequisite. - Hands-on experience with Terraform or another infrastructure-as-code tool, including code review, state-aware changes, environment management, and safe deployment practices. - Comfortable using Git-based development workflows, pull requests, automated testing, and CI/CD pipelines to ship changes safely. - Good working knowledge of Kubernetes and container-based deployments. You should be comfortable understanding deployments, pods, services, logs, configuration, and common failure modes. Experience with EKS is especially valuable. - Skilled at using logs, metrics, and alerts to investigate failing systems, reason through incomplete information, and identify likely root causes. - Familiarity with RBAC, IAM, least-privilege access, or governance controls in at least one platform — cloud, warehouse, BI, or application-level systems. - Attention to detail, curiosity, good judgment, and genuine eagerness to learn unfamiliar tools and systems. - Experience working with vendor support, production incidents, severity-based escalation, and operational follow-through. - Willingness to participate in on-call and after-hours support as needed. You understand that reliable platforms require thoughtful operations, not just build work. - Clear written communication and the ability to work effectively with engineers, analysts, and data stakeholders in a remote-first team. Benefits - The base salary range for this position is $90,700 - $113,400. Range shown in $CAD for Canadian residents. Other countries will differ. Range may vary on a number of factors including, but not limited to: location, experience and qualifications. - Tucows believes in a total rewards offering that includes fair compensation and generous benefits.

Role Description As a Principal Camera Engineer at Otto Aerospace, you will lead the development of advanced camera and sensor systems for the Phantom 3500 Super Natural Vision Infotainment System (SNVIS). This role involves: - Tracking top-level features, requirements, and specifications for camera and sensor integration. - Managing vendor engagements and driving the engineering development and review of design for all hardware and software components related to imaging and sensing technologies. - Capturing and tracking system requirements, including image quality, sensor calibration, data transmission and processing requirements, and thermal performance. - Managing interface definitions between camera and sensor systems and aircraft systems, including the Cabin Management System and avionics. This position requires a strong background in camera and sensor engineering, with experience in shipping high-quality, user-facing imaging and sensing technology products. Qualifications - Master’s degree in Electrical Engineering, Computer Science Engineering, Applied Physics or a related field. - 10+ years in camera and sensor engineering roles for shipped/released products in the aviation, consumer electronics, or related industries. - Experience shipping high-quality, consumer-facing imaging and sensing technology products. - Experience managing complex systems architecture requirements and specifications for consumer electronics product platforms or IFE/avionics platforms, including experience with: - Display panels - Touch panel system - SOCs & system I/O - Cameras, optical sensing - Inertial sensing - Audio (microphone + speaker) systems - Operating Systems and Application Software - Passion for aviation. - Proven track record of leading projects and teams in the development of imaging systems. Requirements - Expertise in camera and sensor technologies. - Strong knowledge of image processing and computer vision. - Excellent problem-solving and analytical skills. Benefits - Competitive salaries. - Subsidized medical, dental, and vision coverage. - 401(k) opportunities. - Paid short term disability. - Voluntary long-term disability and additional term life. - 15 paid days off. - 13 paid holidays. - Paid sick leave. - Potential bonuses and stock options depending on seniority and role.

Role Description As a Senior Machine Learning Engineer (individual contributor) at Revecore, you will: - Use your expertise in machine learning, exploratory data analysis, and software engineering to enhance the productivity and efficiency of our underpayment business. - Work on projects with purpose, such as prioritizing claims based on expected recovery dollars and improving our claim-remit matching process. - Own end-to-end development, training, deployment, evaluation, and improvement of machine learning systems to rank claim opportunities. - Analyze and explore data to identify actionable opportunities from internal and 3rd party data. - Research, implement, and launch new model architectures that drive business impact. - Partner and collaborate with cross-functional teams of software engineers, data engineers, subject matter experts, product managers, and analysts to design and build practical solutions. - Implement cloud MLOps and AIOps best practices to streamline the development, deployment, and maintenance of machine learning models. - Continuously measure the impact of the AI-enabled workflows on key business metrics and use these measurements to improve the machine learning models and workflows. - Learn from and teach your teammates, being the team’s expert in your specialization. - Own a workstream, being the technical lead for the workstream while partnering with others to deliver. Qualifications - A bachelor's degree in any data-centric field; scientific thinking is a must. - Experience working in a similar role, with a focus on machine learning or data science. - Experience developing and deploying machine learning models in a production environment. - Strong experience with Python, including scikit-learn; TensorFlow or PyTorch is a plus. - Ability to wrangle data, perform exploratory data analysis, and draw insights from visualizations. Requirements - An urge to question assumptions and to get it right (or at least good enough) even if your first idea is wrong. - A commitment to collaborate, rather than go off in a corner only to appear when you need to submit a pull request. Benefits - Comprehensive medical, dental, vision, and life insurance benefits from the start of your employment. - 12 paid holidays and flexible paid time off. - 401(k) contributions. - Employee Resource Groups that build community. - Career growth opportunities. - An excellent work/life balance. Work at Home Requirements - A quiet, distraction-free environment to work from in your home. - A secure home internet connection with speeds >20 Mbps for downloads and >10 Mbps for uploads is required. - The workspace area accommodates all workstation equipment and related materials and provides adequate surface area to be productive.

Role Description At Keen, the Senior Algorithms Engineer develops, deploys and improves the algorithms that run in Keen AI Cortex. You're a software engineer first, with the understanding of mathematics, data, and inference to read a model spec, question it, and suggest changes the Principal Algorithms Architect hadn't considered. You'll work directly with the Lead Engineer and Principal Algorithms Architect, taking modeling ideas from prototype to production code and contributing to the algorithms when you see a better path. What you’ll do as the Senior Algorithms Engineer: - Own algorithmic features end-to-end across the next generation of Keen's AI Cortex. Design, implement, test, ship, and watch them run in production. - Pitch in on adjacent Python backend projects (Django and FastAPI APIs, AWS infrastructure) when a piece of work spans more than one service. - Keep the engineering practices tight: code review, tests, CI gates, documentation, and a clean ticketed workflow. - Use AI coding tools with judgment. Fast iteration is the point. So is catching the moments when the model is confidently wrong. - Develop algorithms from business ideas and turn them into production code. - Collaborate with others in the development of the solutions, including the ability to effectively push back when the implementation can be improved. - Communicate clearly across Product, Research, and Engineering. Write the design doc, name the tradeoff, get the decision made. Qualifications - 7+ years shipping production Python on a team. - Knowledge of inference methods, particularly in economics and business domains including time-series models (or more generally state-space modeling), probability modeling, and multivariate systems. - Comfortable with the engineering basics: tests, CI, code review, documentation, and working out of tickets. - Hands-on experience with AWS, Docker. - Operates independently. Scopes own work, talks to stakeholders, makes judgment calls when no one else is available to make them. - Writes and speaks clearly. - Math foundation in linear algebra, probability, and statistics. Demonstrated ability to read and write mathematical specifications, interpret and improve documented solutions. - Working knowledge of pandas and NumPy, with understanding of the use of these packages in implementing algorithms. Requirements - Nice-to-have: Marketing Mix Modeling (MMM) or another econometric background. - Bayesian inference experience. - Optimization exposure. - A track record of developing mathematical algorithms into shipped production systems. Benefits - Mentoring and training from people who've been doing this for years. - Hands-on work on Bayesian models, optimization, and the data engineering behind them, applied to a product real customers use. - A seat in conversations across departments and at company-wide meetings. - 4-day work week with 3-day weekends. - Fully remote within the United States.