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Developer AI/ ML
2 weeks ago
Data Engineer
Must-Have Technologies & Techniques
- Python and SQL for ETL, analytics, and data-centric development
- Kafka (or Solace / Pulsar) with producers, consumers, and exactly-once semantics
- Elasticsearch
- Spark Structured Streaming or Apache Flink for stateful stream/batch processing and windowing
- Airflow (or another DAG scheduler)
- Apache Beam, Debezium CDC, and Iceberg/Delta Lake for ACID data-lake workloads
- ClickHouse (or another time-series DB) with Parquet/ORC stored on MinIO / S3 object storage
Nice-to-Have Technologies & Techniques
- Qdrant, Milvus, or Elasticsearch vector indices for retrieval-augmented generation (RAG)
- Prometheus metrics and Grafana dashboards for pipeline and cluster monitoring
- Feast, RedisFeatureStore, or custom metadata-driven feature registries
- Apache Beam, Debezium CDC, and Iceberg/Delta Lake for ACID data-lake workloads
- Terraform, Crossplane, Pulumi, and Kubernetes Operators (Kubebuilder) for IaC
- Loki log aggregation and OpenTelemetry tracing for enhanced observability
- gNMI/gRPC collectors, NetFlow/IPFIX parsers, SNMP polling frameworks for telco data