Sr. Data Engineer - Flink

Building 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.

Requirements

• 7+ years of experience in data engineering and software development
• Ability to write high-quality code in Java/Scala, Python, or equivalent languages
• Deep, hands-on production experience with Apache Flink — Data. Stream 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 TTL
• Hands-on experience with checkpointing, savepoints, and fault tolerance — exactly-once vs. at-least-once semantics, recovery, and savepoint-based job upgrades
• Strong grasp of event-time processing: watermarking, windowing strategies, allowed lateness, and late-data handling
• Experience diagnosing and resolving backpressure — parallelism, operator chaining, and network buffer tuning
• Experience operating Flink on self-managed infrastructure (Kubernetes or YARN) — application vs. session mode, high availability, and rolling upgrades
• Practical experience with stream processing (Kafka Streams or equivalent) and messaging systems for high-volume workloads, including exactly-once sinks and schema registry usage
• Practical experience with distributed query engines (e.g., Trino/Presto or similar)
• Practical experience with ETL / data integration tools, commercial or open-source (e.g., Datastage, Informatica, Apache Ni. Fi, or similar)
• Practical experience with SQL-based transformation frameworks (e.g., dbt or others)
• Strong SQL skills and understanding of data modeling and data warehousing for analytical workloads
• Hands-on experience with real-time / low-latency analytical stores (columnar or OLAP engines, e.g., Apache Pinot/Click. House 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)
• Experience with workflow orchestration tools (e.g., Airflow or similar)
• Familiarity with data lake table formats (e.g., Apache Iceberg or similar), including streaming ingestion, compaction, and small-file management
• Familiarity with data governance / cataloging tools (e.g., Data. Hub or similar)
• Familiarity with lakehouse management systems (e.g., Apache Amoro or similar)
• Familiarity using AI tools for development and debugging (Claude, Cursor, Codex)