Data Engineer

Role Overview

Own the design, implementation, and scaling of core data infrastructure and reliable ETL pipelines that power analytics and machine learning.

Responsibilities

• Design, build, and optimize scalable data pipelines using Python, SQL, and Apache Airflow to ingest data from diverse sources.
• Architect and maintain data warehouse models and transformations with dbt, using Snowflake or BigQuery (and related stack).
• Implement data quality checks, automated monitoring, and alerting for pipeline failures and schema drift.
• Collaborate with engineering teams to define data collection standards and integrate new product events smoothly.
• Optimize query performance, data storage costs, and pipeline latency across distributed processing frameworks.
• Write clean, version-controlled code, participate in peer code reviews, and document data architecture best practices.

Requirements

• 3–6 years of experience in data engineering, backend engineering, or a closely related technical domain.
• Strong SQL and Python skills; experience building production-grade ETL pipelines.
• Hands-on experience with modern data stack tools: dbt, Airflow, Snowflake, BigQuery, and/or Databricks.
• Solid understanding of data modeling, including dimensional modeling and data warehouse design principles.
• Bachelor’s degree in Computer Science, Statistics, Engineering, or equivalent practical experience.

Nice to Haves

• Experience with real-time streaming (e.g., Apache Kafka or Flink).
• Infrastructure as Code experience (e.g., Terraform).