Data Engineer (Apache Flink)

🏢 Aptivamena Tech
📍 Cairo, EgyptFull-timeOn-site
📅 Posted: 3w ago🔄 Updated: 3w ago
CV%
✨ AI Summary
This role is for a Data Engineer specializing in Apache Flink, focused on building high-scale, low-latency streaming data pipelines on self-managed infrastructure. The candidate will design and operate real-time data systems end-to-end, utilizing Apache Flink as the core stream-processing engine. Key responsibilities include managing Flink state, ensuring fault tolerance, and optimizing for event-time processing. Experience with Kafka, distributed query engines, ETL tools, and real-time analytical stores is crucial. Familiarity with containerization, orchestration tools, and data lake technologies is also expected.
Required Skills
Information Technology
JavaScalabilityPythonSQLApacheMessaging SystemsETLData IntegrationData ModelingData WarehouseData PlatformsDockerKubernetesApache Airflow
Other
DataStream APITable APIFlink SQLcheckpointingfault toleranceevent-time processingstream processingdistributed query enginesreal-time analytical stores
Soft Skills & Professional Competencies
Time Management
Business, Sales & Management
HR Management
Requirements
7+ years of experience in data engineering and software developmentAbility to write high-quality code in Java/Scala, Python or equivalent languagesDeep, hands-on production experience with Apache Flink - DataStream API and Table API / Flink SQL (core requirement)Demonstrated experience with Flink state management: keyed state, state backends (e.g. RocksDB), large state sizes and state TTLHands-on experience with checkpointing, savepoints and fault tolerance - exactly-once vs at-least-once semantics, recovery and savepoint-based job upgradesStrong grasp of event-time processing: watermarking, windowing strategies, allowed lateness and late-data handlingExperience diagnosing and resolving backpressure - parallelism, operator chaining and network buffer tuningExperience operating Flink on self-managed infrastructure (Kubernetes or YARN) - application vs session mode, high availability and rolling upgradesPractical experience with stream processing (Kafka Streams or equivalent) and messaging systems for high-volume workloads, including exactly-once sinks and schema registry usagePractical experience with distributed query engines (e.g. Trino/Presto or similar)Practical experience with ETL / data integration tools (e.g. Datastage, Informatica, Apache NiFi or similar) and SQL-based transformation frameworks (e.g. dbt)Strong SQL skills and understanding of data modeling and data warehousing for analytical workloadsHands-on experience with real-time / low-latency analytical stores (columnar or OLAP engines, e.g. Apache Pinot/ClickHouse or similar)Practical experience with big-data platforms and distributions (e.g. Cloudera, Hadoop ecosystem or similar)Practical experience containerizing and operating data workloads (Docker; Kubernetes a plus) and workflow orchestration tools (e.g. Airflow)Familiarity with data lake table formats (e.g. Apache Iceberg), data governance / cataloging tools (e.g. DataHub) and lakehouse management systems (e.g. Apache Amoro)Familiarity using AI tools for development and debugging (Claude, Cursor, Codex)
Description
Build high-scale, low-latency streaming data pipelines deployed on infrastructure we run ourselves (on-prem), not managed cloud services. You will design and operate high-volume real-time data systems end to end, with Apache Flink as the core stream-processing engine.
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🎯 Overalli74%
⚡ Skillsi85%
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Ontology Match: 85.0
Matched:✓ Requirements Matching✓ Ontology Skills Mapping
📜 Eligibilityi49%
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Local: 19600%
🏗️ Career Fiti91%
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Seniority: 91.0
📋 Requirementsi67%
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Domain: 67.0
🔥 Motivationi78%
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Title Fit: 78.00