Data Engineer(Snowflake & Apache Doris) - Fulltime

We are seeking an experienced Data Engineer to design, build, and optimize scalable data pipelines and analytics infrastructure. The ideal candidate has hands-on expertise with Snowflake for cloud data warehousing and Apache Doris for real-time OLAP analytics, and is comfortable working across the full data lifecycle — from ingestion to transformation to serving high-performance queries for analytics and BI teams.

Responsibilities

- Design, build, and maintain robust, scalable ETL/ELT pipelines to ingest structured and semi-structured data from multiple sources into Snowflake and Apache Doris.

- Architect and optimize data models, schemas, and partitioning strategies in Snowflake and Doris to support high-throughput, low-latency analytical workloads.

- Tune query performance, indexing, materialized views, and storage layout in Doris for real-time OLAP use cases.

- Manage Snowflake warehouse sizing, cost optimization, resource monitors, and query performance tuning.

- Build and maintain data orchestration workflows (e.g., Airflow, dbt, or similar) to automate pipeline scheduling, dependency management, and monitoring.

- Implement data quality checks, validation frameworks, and observability/alerting for pipeline reliability.

- Collaborate with data analysts, data scientists, and BI teams to understand reporting requirements and translate them into performant data models.

- Support migration and integration projects between transactional systems, data lakes, Snowflake, and Doris.

- Ensure data governance, security, and access control best practices (RBAC, data masking, encryption) across platforms.

- Document data architecture, pipeline logic, and operational runbooks.

Required Qualifications

- 7+ years of experience as a Data Engineer or in a similar role.

- Hands-on production experience with Snowflake — including SQL scripting, warehouse management, Snowpipe, Streams/Tasks, and performance tuning.

- Hands-on experience with Apache Doris — including cluster architecture (FE/BE nodes), table models (Aggregate, Unique, Duplicate), materialized views, and real-time data loading (Stream Load, Routine Load, Broker Load).

- Strong SQL skills and experience with data modeling (star/snowflake schemas, dimensional modeling).

- Proficiency in Python commonly used in data engineering.

- Experience with workflow orchestration tools (Apache Airflow, dbt, Dagster, or similar).

- Familiarity with streaming/messaging systems (Kafka, Pulsar, or similar) for real-time data ingestion.

- Solid understanding of data warehousing concepts, OLAP vs. OLTP design, and distributed systems fundamentals.

- Experience with version control (Git) and CI/CD practices for data pipelines.