Data Engineer

About the role

The role owns the design, implementation, and scaling of the core data infrastructure, building reliable pipelines that process high-throughput event data and batch analytics. The data team works closely with analytics engineering and business stakeholders to ensure high data quality, seamless access, and performant data models across the entire tech stack.

Responsibilities

• Design, build, and maintain scalable data pipelines and ETL processes using Python, SQL, and Apache Airflow
• Manage cloud data warehousing infrastructure (Snowflake, BigQuery, or Redshift), optimizing query performance and managing compute costs
• Implement data quality checks, automated monitoring, and data observability frameworks to catch pipeline failures early
• Collaborate with analytics engineers and data scientists to model data for consumption in BI tools and machine learning applications
• Write clean, version-controlled code using Git, participating in code reviews and establishing data engineering best practices

Requirements

• 3–6 years of experience in data engineering, building production-grade data pipelines and data warehouses
• Advanced SQL and Python programming skills, with strong proficiency in orchestration tools like Apache Airflow, Prefect, or Dagster
• Deep experience with cloud data warehouses (Snowflake, BigQuery) and modern data stack tools (dbt)
• Familiarity with distributed data processing frameworks such as Apache Spark, Kafka, or Flink
• Bonus: Experience with infrastructure-as-code (Terraform) and stream processing architectures