Senior Data Engineer
Our Journey So Far
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.
Your Impact
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.
What You’ll Drive Forward
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.
What Powers Your Drive
+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.
Ready to Get on Board?
Help us shape the future of ride-hailing and urban mobility. Submit your CV and let’s build smarter cities together.