Data Engineer

About The Role

The role owns the design, implementation, and scaling of core data infrastructure and reliable ETL pipelines that power analytics and machine learning systems. The team works closely with data analysts, data scientists, and backend engineers to ensure data flows seamlessly, safely, and efficiently across the entire technology stack.

Key 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 using dbt and Snowflake or BigQuery
• Implement robust data quality checks, automated monitoring, and alerting for pipeline failures and schema drifts
• Collaborate with engineering teams to define data collection standards and ensure smooth integration of new product events
• Optimize query performance, data storage costs, and pipeline latency across distributed data processing frameworks
• Write clean, version-controlled code; participate in peer code reviews and document data architecture best practices

What We Are Looking For

• 3–6 years of experience in data engineering, backend engineering, or a closely related technical domain
• Advanced SQL and Python programming skills, with proven experience building production-grade ETL pipelines
• Hands-on experience with modern data stack tools: dbt, Airflow, Snowflake, BigQuery, or Databricks
• Solid understanding of data modeling concepts, dimensional modeling, and data warehouse design principles
• Bachelor's degree in Computer Science, Statistics, Engineering, or equivalent practical experience
• Bonus: Experience with real-time streaming technologies such as Apache Kafka or Flink, and Infrastructure as Code (Terraform)