Data Engineer

About The Role

The role owns the design, implementation, and scaling of core data infrastructure, high-throughput ingestion pipelines, and analytical architectures that power the entire product ecosystem. You will work alongside analytics engineers, backend developers, and product managers to ensure data reliability, optimal query performance, and seamless data availability across teams.

Key Responsibilities

• Architect and maintain robust ETL/ELT pipelines using Python, SQL, and modern orchestration tools like Apache Airflow or Dagster
• Design and optimize cloud-based data warehouses in Snowflake or BigQuery, implementing efficient data modeling and partitioning strategies
• Monitor data pipeline performance, troubleshoot ingestion failures, and establish automated data quality and anomaly detection alerts
• Collaborate with engineering teams to define event-tracking schemas and ensure clean, structured telemetry capture at the source
• Write clean, modular, and version-controlled code, participating in rigorous code reviews and data platform documentation

What We Are Looking For

• 3–6 years of experience in data engineering, backend development, or building large-scale data pipelines in production
• Advanced SQL skills and expert-level proficiency in Python for data processing, scripting, and pipeline automation
• Hands-on experience with modern cloud data warehouses (Snowflake, BigQuery, or Redshift) and orchestration frameworks (Airflow, Prefect, or Dagster)
• Strong understanding of data modeling principles, star schema design, and distributed processing tools like Spark
• Bonus: Experience with streaming architectures using Kafka, infrastructure-as-code with Terraform, or operationalizing ML data pipelines