Real-Time Data Systems Engineer

5 days ago

anand, India beBeeBackend Full time

Backend Communications SpecialistWe are seeking a skilled engineer to design and develop resilient backend services that power real-time analytics and automation at scale.The ideal candidate will be responsible for designing APIs, processing streaming data, and integrating with voice/telephony and cloud services to deliver low-latency, high-reliability experiences.Design and ship backend APIs, background/async jobs, and secure integrations with external services.Build and optimize real-time data pipelines for call/quality metrics; own observability, tracing, and SLOs.Troubleshoot SIP/VoIP issues using packet captures and logs; automate analysis where possible.Containerize workloads with Docker, contribute to CI/CD, and collaborate on cloud deployment.Apply distributed-systems patterns for scale and fault tolerance.Model data in PostgreSQL and MongoDB; tune indexes/queries; maintain migration discipline.Prototype AI/ML features for analytics, anomaly detection, and conversational agents.Key Requirements:Python & Django: RESTful APIs, authentication/authorization, background tasks, testing, and performance tuning.Networking fundamentals: TCP/IP, DNS, TLS, HTTP/2; practical debugging with Wireshark; familiarity with NetFlow/SNMP.Telephony domain: SIP signaling, RTP/RTCP, call flows, PSTN/VoIP basics; exposure to SBCs.Cloud & DevOps: Working knowledge of Azure/AWS, Docker; Kubernetes exposure is a plus; comfort with CI/CD and secrets management.Distributed systems: Microservices, event-driven design (queues/streams), horizontal scaling, resilience patterns.Databases: Hands-on with PostgreSQL and MongoDB (schema design, indexing, migrations, backups/restore).Mindset: Ownership, bias to automate, clear writing, and a product quality bar that values reliability and latency.Nice to Have:Kubernetes, IaC, metrics/log stacks, OpenTelemetry.Experience with Microsoft Graph ecosystems, webhooks, or real-time comms SDKs.Practical AI/ML: time-series anomaly detection, clustering, or LLM-powered workflows.Success Profile:30 days: Ship a well-tested service with one external integration; add dashboards/alerts for core SLOs.60 days: Deliver an automated SIP call-flow parser or PCAP → insights tool; reduce a p99 latency hot spot.90 days: Lead a small initiative end-to-end; document runbooks, and mentor a junior dev.

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