Data Engineer

About the role

The role owns the design, implementation, and scaling of core data infrastructure and streaming pipelines that power analytics and machine learning systems. The team works closely with software engineering and product analytics to ensure high-throughput data processing, robust data modeling, and strict data quality standards.

Responsibilities

• Design and build scalable ETL/ELT pipelines using Python, SQL, and Apache Spark to ingest data from diverse sources into a cloud data warehouse
• Own data warehouse modeling and architecture using modern tools like Snowflake, BigQuery, or dbt, ensuring optimal query performance and data governance
• Monitor data pipeline health and reliability, implementing automated alerting and recovery mechanisms for data drift or latency spikes
• Collaborate with analytics and engineering teams to define data contracts, schemas, and metric definitions for business-critical dashboards
• Write clean, testable, and well-documented infrastructure-as-code and pipeline orchestration code using tools like Airflow or Prefect
• Participate in code reviews, contribute to data engineering best practices, and optimize existing data workflows for cost and performance

Requirements

• 3–6 years of experience in data engineering, backend development, or analytics engineering with a focus on large-scale data systems
• Advanced SQL and Python programming skills, with proven experience building production-grade data pipelines
• Hands-on experience with modern cloud data warehouses (Snowflake, BigQuery, Redshift) and transformation tools (dbt)
• Familiarity with orchestration tools such as Apache Airflow, Prefect, or Dagster, and containerization technologies like Docker
• Solid understanding of data modeling principles, dimensional modeling, and data quality frameworks
• Bonus: Experience with real-time streaming technologies like Kafka or Flink, and infrastructure-as-code tools such as Terraform