{"id":4152915,"url":"https://alion.io/job/a-listware-seniorlead-data-engineer","title":"Senior/Lead Data Engineer","company":{"id":4485862,"name":"A-Listware","domain":"a-listware.com","url":"https://alion.io/company/a-listware","size_band":"51-200","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Ukraine"],"countries":["UA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Apache Kafka","optional":false},{"name":"CI/CD","optional":false},{"name":"ClickHouse","optional":false},{"name":"Docker","optional":false},{"name":"Flink","optional":false},{"name":"gRPC","optional":false},{"name":"Java","optional":false},{"name":"Kubernetes","optional":false},{"name":"PostgreSQL","optional":false},{"name":"React.js","optional":false},{"name":"Redis","optional":false},{"name":"Time Series","optional":false},{"name":"TypeScript","optional":false},{"name":"JavaScript","optional":true}],"status":"live","first_seen_at":"2026-11-12T09:45:25Z","employer_posted_date":null,"last_verified_at":"2026-11-12T09:45:25Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"Our Customer:\n\nOur customer develops a digital employee experience platform that monitors endpoint and virtual desktop performance for enterprise organizations. The platform handles millions of events per minute across an international customer base.\nYour Tasks:\n\nBuild production-grade Java services and high-throughput data-processing components\n\nDevelop, optimize and maintain large-scale Kafka and Apache Flink streaming pipelines\n\nIntegrate data from multiple products into a unified organizational data model\n\nImplement employee, department, device and RBAC-related functionality\n\nBuild services and APIs using Java, TypeScript, gRPC and Protobuf\n\nDesign and optimize persistence on PostgreSQL, ClickHouse and Redis, including schemas, indexes and data-access patterns\n\nImprove scalability, reliability, throughput, latency, observability and operational cost\n\nDiagnose production bottlenecks across distributed pipelines, services and databases\n\nDevelop TypeScript and React frontend capabilities when needed\n\nCollaborate with architecture, product and DevOps teams\n\nUse AI-assisted and agentic development tools while maintaining correctness, security and code quality\nRequired Experience and Skills:\n\n6+ years of experience building and maintaining production distributed, high-throughput systems\n\n5+ years of professional Java experience, including concurrency, memory management and JVM performance tuning\n\n3+ years of hands-on production experience with Apache Kafka, including partitioning, consumer groups, schemas, delivery guarantees, fault tolerance and throughput tuning\n\n2+ years of hands-on production experience with Apache Flink, including stateful processing, windowing, checkpointing, state management, backpressure and fault recovery\n\nProven track record of personally building, scaling and operating systems processing millions of events per minute\n\nDeep understanding of time-series and large-scale analytical databases, including modeling, ingestion, query tuning and partitioning\n\nHands-on experience with PostgreSQL or another relational database\n\nStrong command of Protobuf, including schema evolution and compatibility\n\nWorking knowledge of gRPC and service-to-service communication\n\nHands-on experience deploying, monitoring and troubleshooting distributed workloads on Kubernetes\n\nHands-on experience with Docker and CI/CD pipelines\n\nSolid understanding of production observability and incident resolution\n\nHands-on use of AI-assisted and agentic coding tools in production, including critical review of generated code\n\nProven ability to deliver independently, make architectural tradeoffs and identify risks early\n\nStrong communication skills for explaining technical decisions and working with international teams\n\nEnglish – Upper-Intermediate\nWould Be a Plus:\n\nHands-on experience with ClickHouse\n\nHands-on experience with Redis\n\nHands-on experience with TypeScript and 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