Senior Data Engineer

At Snapp, we’re redefining how cities move. Our ride-hailing and mobility platform connects millions of riders and drivers every day, delivering safe, reliable, and efficient transport solutions. Powered by real-time data and robust infrastructure, we make urban travel faster, simpler, and more sustainable.

We operate with the mindset of a global tech leader and the agility of a startup, building services that scale across markets while staying responsive to local needs.

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.

• +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.

Help us shape the future of ride-hailing and urban mobility. Submit your CV and let’s build smarter cities together.