Ripple is our centralized Customer Data Platform (CDP) and event-tracking engine at Tapsi. It serves as the backbone for Tapsi Group and its subsidiaries, powering high-throughput event ingestion, marketing automation triggers, and near-real-time analytical query serving at scale.
As a Data Engineer (Mid-Level) on the Growth Engine team, you will contribute to the design, development, and operation of our distributed streaming, storage, and lakehouse infrastructure. You will work on high-throughput pipelines, data processing systems, and cluster operations that support event ingestion, data quality, and analytical workloads across the group.
Responsibilities
Data Infrastructure & Operations: Help deploy and operate robust data infrastructure across environments, including tools such as Kafka, StarRocks, Iceberg, and Airflow on Kubernetes.
Lakehouse & Data Warehouse Architecture: Contribute to building and optimizing our lakehouse footprint using technologies such as Apache Iceberg, HDFS/Object Storage, and StarRocks or similar OLAP systems.
Workflow Orchestration & Batch Pipelines: Design, implement, and maintain reliable batch and maintenance pipelines using Apache Airflow.
Stream Processing & Ingestion: Build and maintain low-latency streaming services in Go, Kotlin, or Java that consume from Kafka and feed downstream systems.
Schema Governance & Data Quality: Work with schema-registry pipelines such as Avro/Protobuf, validation rules, and dead-letter queue handling.
Cross-Cluster Routing & Replication: Support event routing and replication workflows across clusters and environments.
Performance Tuning & Observability: Help monitor and tune data pipelines and systems using Prometheus, Grafana, and OpenTelemetry.
Technical Collaboration: Participate in RFCs, design discussions, and implementation reviews while following engineering best practices.
Requirements
Experience: 3+ years of experience in Data Engineering or distributed backend systems.
Programming & Systems: Strong proficiency in Go, Kotlin, or Java, with a good understanding of concurrency and writing maintainable code.
SQL & Database Expertise: Solid SQL skills, including joins, CTEs, and window functions, plus experience with RDBMS systems such as PostgreSQL or MySQL.
Distributed Streaming: Hands-on experience with Kafka or similar distributed streaming platforms.
Lakehouse & Modern Storage: Familiarity with data lakehouse or warehouse technologies such as Apache Iceberg, HDFS, StarRocks, ClickHouse, or Druid.
Workflow Orchestration: Experience developing or debugging workflows in Apache Airflow.
Infrastructure & Cluster Ops: Working knowledge of Kubernetes, Helm, and Linux environments.
Data Modeling & Schemas: Familiarity with Avro or Protobuf and the basics of schema evolution and event-driven modeling.
Spark & Flink: Familiarity with Apache Spark and Apache Flink for batch and stream processing.
Mindset: Strong ownership, curiosity, and a practical approach to reliability and performance.
Nice to Have
Experience with CDP or marketing automation pipelines.
Familiarity with BI/visualization tools such as Apache Superset.
Exposure to GitOps workflows such as ArgoCD or Flux.
Experience with Terraform or other infrastructure provisioning tools.
تپسی در خرداد ۱۳۹۵ با یک تیم ۱۰ نفره از متخصصان ایرانی در حوزه برنامهنویسی، طراحی و توسعه محصول فعالیتش رو آغاز کرد و اولین نسخه اپلیکیشن رو به کاربران ارائه داد. امروز تپسی بهعنوان یک سوپر اپلیکیشن، علاوه بر خدمات سفرهای آنلاین درون و برون شهری و ارسال فوری مرسولات، خدمات متنوعی از جمله تپسیفود، تپسیشاپ، تپسی گاراژ و... رو در دل خودش جا داده و همچنان در حال گسترش و توسعه فعالیتهاشه.
تپسی با ۲۰ میلیون کاربر در بیش از ۲۸ شهر ایران و تیمی متشکل از بیش از ۱۰۰۰ نفر در سراسر کشور، هر روز در حال رشد و پیشرفته. اعضای تیم ما با تخصصهای منحصربهفردشون، رضایت کاربران و ایجاد تغییرات مثبت در زندگی افراد جامعه رو بهعنوان منبع انگیزه و انرژی برای مواجهه با کارهای چالشبرانگیز روزانه میدونن. ما همیشه به دنبال فرصتهای جدید برای بهبود و پیشرفت هستیم و تمام تلاشمون اینه که از مسیر یادگیری خارج نشیم. تپسی همون جاییه که میتونی از خودت سبقت بگیری!