About the Role:
We are looking for a Mid/Senior Data Engineer to build, operate, and improve our next-generation data platform. You will design reliable batch and streaming data pipelines, build scalable lakehouse architectures, and develop the infrastructure that powers analytics and operational data products.

You will work with technologies including Spark, Trino, Iceberg, Kafka, dbt, Airflow, Kubernetes, PostgreSQL, and Debezium while collaborating closely with Product, Backend, and Data teams.

What You'll Do:
Build Data Platforms
Design, build, and maintain scalable batch and streaming data pipelines.
Build and optimize lakehouse architectures using Iceberg or Delta Lake.
Develop distributed data processing jobs with Spark and Trino.

Operate Production Systems
Build and maintain CDC pipelines using Debezium and Kafka Connect.
Manage Airflow workflows and Kubernetes deployments.
Improve platform scalability, performance, and reliability.

Ensure Reliability
Implement monitoring, alerting, and data quality checks.
Investigate production incidents and perform root cause analysis.
Optimize SQL queries, Spark/Trino jobs, and storage layouts.

Collaborate
Work with Product, Backend, and Data teams on data contracts and schema evolution.
Document platform architecture, pipelines, and operational procedures.

What We're Looking For
Programming: Python (required), Scala (preferred), Java or Go (a plus)
Data Processing: Strong SQL, dbt, Apache Spark, Trino
Data Platform: Apache Kafka, Kafka Connect, Debezium, ETL/ELT pipelines
Storage & Lakehouse: PostgreSQL, Apache Iceberg or Delta Lake
Infrastructure: Kubernetes, Linux, Docker
Workflow & Automation: Apache Airflow
Observability: Grafana, Zabbix
Engineering Practices: Data modeling, distributed systems, performance tuning, debugging, monitoring, documentation, and CI/CD

Nice to Have
Experience with Hive Metastore
Experience with distributed file systems/object stores (HDFS preferred)
Experience building internal data platforms
Experience with data observability, lineage, or metadata management
Experience contributing to open-source projects

Success in This Role
Production data pipelines achieve high reliability and meet SLA targets.
Batch and streaming workloads are scalable and well monitored.
Platform incidents are detected quickly and resolved effectively.
Lakehouse tables remain performant and maintainable.
Engineering teams can confidently deploy and operate data workloads.

Core Technology Stack
Programming: Python, Scala, Java (plus), Go (plus)
Processing: SQL, Spark, Trino, dbt
Platform: Kafka, Kafka Connect, Debezium, Airflow
Storage: PostgreSQL, Iceberg, Delta Lake
Infrastructure: Kubernetes, Linux, Docker
Monitoring: Grafana, Zabbix