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Senior Data Engineer

  • On-site
    • Tehran, Tehrān, Iran, Islamic Republic of
  • Tech

Job description

As a Senior Data Engineer, you will design, build, and maintain scalable data infrastructure and pipelines that handle billions of records each day. You will ensure fast, reliable, and high-quality data flows across our lakehouse platform, supporting both streaming and batch processing. Your work will be essential in enabling dependable data access, powering analytics, and accelerating AI-driven initiatives across the organization.

  • Design and maintain large-scale ETL/ELT pipelines in Apache Flink, Ariflow and Spark for both streaming and batch workloads.

  • Build and optimize real-time streaming systems using Kafka.

  • Develop scalable ingestion frameworks for Delta Lake, Iceberg, and Hudi.

  • Manage and optimize Ceph-based object storage within our data lakehouse.

  • Oversee ClickHouse operations to ensure high-performance analytical querying.

  • Drive reliability, scalability, and cost efficiency across systems handling billions of daily records.

  • Deliver production-grade code in Python, Go, or Java.

  • Implement data quality, monitoring, and observability frameworks.

  • Collaborate with ML/AI teams to support model training, feature pipelines, and inference workflows.

  • Reduce data pipeline latency by implementing efficient streaming architectures.

  • Optimize storage costs while maintaining query performance across lakehouse layers.

Job requirements

  • +6 years of experience in data engineering roles.

  • Strong proficiency in at least two programming languages: Python, Go, or Java.

  • Hands-on experience with Kafka and stream processing (Flink or Spark Streaming).

  • Solid understanding of Spark and distributed computing.

  • Experience with at least one lakehouse table format (Delta Lake, Iceberg, or Hudi).

  • Strong SQL skills and experience with analytical databases (ClickHouse or similar columnar databases).

  • Experience with DataOps practices for managing production environments, including Infrastructure as Code (e.g., Terraform, Ansible) and GitOps-based deployment strategies (e.g., Kubernetes, ArgoCD).

  • Strong understanding of data modeling, data warehousing concepts, and ETL best practices

  • Experience with version control (Git) and CI/CD practices.

  • Strong problem-solving abilities and analytical thinking.

  • Excellent collaboration and communication skills.

  • Adaptability to rapidly evolving technology landscape.

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