Data Engineer

Role Overview

Own the design, implementation, and scaling of core data infrastructure, high-throughput ingestion pipelines, and modern data warehouse architecture. Partner with analytics, product, and engineering stakeholders to ensure reliable, high-quality data across business units.

Responsibilities

• Architect and build robust, scalable ETL/ELT pipelines using Python, SQL, and Apache Airflow
• Model and optimize large-scale datasets in Snowflake, BigQuery, or Redshift, focusing on performance, cost efficiency, and usability
• Implement automated data quality tests, data lineage tracking, and monitoring to detect and resolve pipeline failures quickly
• Collaborate with software engineering teams on database schema design, event streaming architectures, and API integrations
• Maintain technical documentation for data models, system architecture, and operational runbooks
• Participate in code reviews, mentor junior engineers, and promote data engineering best practices

Requirements

• 3–6 years of professional experience in data engineering, backend engineering, or analytics engineering
• Strong SQL and Python skills for data manipulation, automation, and pipeline development
• Hands-on experience with cloud data warehouses: Snowflake, BigQuery, or Amazon Redshift
• Proficiency with workflow orchestration tools such as Apache Airflow, Dagster, or Prefect
• Solid understanding of data modeling (including dimensional modeling) and data governance

Nice to Haves

• Experience with streaming technologies like Apache Kafka or Flink
• Experience with infrastructure-as-code tools like Terraform