• Dive deep into transforming our IaC to support cross region resilient AWS infrastructure. • Develop and maintain Infrastructure as Code (Terraform), ensuring reusable modules, environment consistency, and automated provisioning of AWS infrastructure. • Operate and support Kubernetes (EKS) platforms, including cluster lifecycle management, workload deployment, scaling, and container runtime best practices. • Design, implement, and operate standardized CI/CD platforms using GitHub Actions and GitOps tooling (ArgoCD), supporting multi-environment deployments, rollback, and automation. • Implement GitOps deployment patterns and declarative configuration management. • Partner with engineering teams to integrate deployment standards into the application lifecycle. • Produce and maintain documentation and usage standards. • Provide technical mentorship and guidance on DevOps tooling and practices. • Participate in on-call rotation as required.

• Define and evolve DevOps architecture in complex environments • Develop and maintain CI/CD pipelines (GitLab, Jenkins, Azure DevOps) • Automate infrastructure using Terraform, Ansible and Packer • Manage and operate environments on AWS, Azure and Google Cloud Platform • Deploy and administer solutions with Docker, Kubernetes and OpenShift • Perform advanced troubleshooting in Linux, Windows and cloud environments • Implement Infrastructure as Code (IaC) and GitOps practices • Create and evolve monitoring and observability strategies (Prometheus, Grafana, Datadog, New Relic, Kibana) • Ensure high availability, performance and scalability of systems • Support development teams with DevOps best practices • Lead technical initiatives and serve as a technical reference for the team

• Own SLOs, SLIs, and error budgets for all production services; drive error budget discipline across engineering • Design reliability patterns for AI agent pipelines: LLM observability, tool-use tracking, failure detection, and graceful degradation • Architect for blast radius containment — agent failures must have bounded customer impact through isolation, circuit breaking, and rapid recovery • Mature our Canada Central/West active-active architecture toward 24-hour RTO with full regional failover • Lead incident response and post-incident reviews that produce durable fixes; maintain DR procedures through regular testing • Serve as the primary reliability liaison to Software and AI Engineering, translating requirements into actionable standards • Partner with AI Engineering on compute provisioning, model serving, inference latency, and workload isolation • Own CI/CD pipeline strategy (Bitbucket Pipelines, GitHub Actions) — set standards, optimize deployment frequency, and ensure teams can ship confidently • Drive IDP adoption and enable teams on SRE practices: on-call readiness, SLO definition, runbook development, and self-service tooling • Represent reliability in architectural discussions; surface risk before it's committed to design • Own the service catalog — a living inventory of all services, AI agents, dependencies, ownership, and SLOs • Operate Datadog as the single pane of glass for service health, infrastructure, and agentic pipeline telemetry • Build golden path templates in Backstage and/or Atlassian Compass so teams ship reliably without routine SRE involvement • Apply AIOps in Datadog to automate anomaly detection, incident triage, and remediation recommendations • Own infrastructure as code via Terraform and GitOps; enforce IaC policy in partnership with Trust Assurance • Own FinOps visibility into AWS cost segments; model cloud cost impact as AI/ML workloads scale • Formally mentor junior and intermediate SRE engineers, with accountability for their technical growth and career progression • Build AI-assisted automation to progressively reduce toil and scale the team's operational capacity

Role Description As a Senior Data Engineer, you will design, build, and tune the data layer that powers our clients' mission-critical applications on Azure and SQL Server. You will own complex query performance, indexing strategy, and concurrency design, and you will architect the data access patterns that connect application code to the database through linq2db, LINQ-to-SQL, Entity Framework, and ADO.NET. You will partner with application engineers, architects, and product teams to deliver high-throughput, low-latency data solutions, and you will mentor others on database design, query optimization, and modern data engineering practices. Qualifications - A database craftsperson who treats query performance, indexing, and concurrency as first-class engineering concerns rather than afterthoughts. - A clear communicator who can explain execution plans, locking behavior, and data access trade-offs to engineers, architects, and product stakeholders. - Comfortable operating with ambiguity, capable of profiling production workloads and proposing concrete solutions backed by evidence. - A mentor who raises the bar for the team through code review, query review, and pattern guidance. - Customer-obsessed and outcome-focused, balancing delivery speed with the long-term health and scalability of the data platform. Requirements - Bachelor's Degree in Computer Science or a related discipline, or equivalent experience; MUST be proficient in written and spoken English (85%). - 5 to 8 years of professional data engineering or back-end engineering experience with a strong database focus. - Expert-level proficiency in SQL on SQL Server 2019+, including complex queries, window functions, set-based operations, query plan analysis, indexing strategy, statistics, RCSI, isolation levels, and Change Data Capture. - Expert-level proficiency in database performance tuning, including bottleneck identification, index design, query rewrites, and concurrency design under production load. - Strong proficiency in C# data access using linq2db, LINQ-to-SQL (DBML), Entity Framework, and ADO.NET; ability to choose the appropriate tool for each scenario and avoid ORM performance pitfalls. - Strong proficiency in Python for data engineering tasks, scripting, and automation. - Hands-on experience with Azure data services (Azure SQL, storage, networking, security) and deploying production data workloads in Azure. - Experience with database CI/CD, schema versioning, and migration tooling. - Solid Git, code review discipline, and familiarity with modern engineering practices including testing and observability. - Experience with Azure Data Factory, Synapse, or other Azure analytics services is a plus. - Experience designing event-driven or streaming data architectures is a plus. - Excellent analytical and problem-solving skills; strong communication, collaboration, customer orientation, innovation mindset, and adaptability under ambiguity. Company Description