Data Engineer - Snowflake & Apache Doris

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.

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.

Preferred Qualifications

• Experience migrating workloads from traditional data warehouses to Snowflake and/or Doris.

• Familiarity with cloud platforms (AWS, GCP, or Azure) and infrastructure-as-code tools (Terraform, CloudFormation).

• Experience with containerization/orchestration (Docker, Kubernetes) for deploying Doris clusters.

• Knowledge of BI/visualization tools (Tableau, Looker, Superset, Power BI).

• Background in cost optimization and capacity planning for large-scale analytical systems.

• Contributions to open-source data engineering projects (e.g., Apache Doris community).

Pay: $57.00 - $59.00 per hour

Experience:

• Data Engineer: 7 years (Required)
• Snowflake: 4 years (Required)
• SQL: 4 years (Required)
• Python: 4 years (Required)
• Apache Doris : 2 years (Required)

$57 - $59 an hour - Full-time, Contract