Data Engineer

About The Role

The role owns the data infrastructure that powers decision-making across the business — building and maintaining batch and streaming pipelines that move hundreds of millions of events per day from production systems into a warehouse optimized for analytics.

The Data Engineer will partner closely with analytics engineers, data scientists, and product teams to model data correctly at the source, ensuring that every dashboard, forecast, and experiment runs on trustworthy, well-documented foundations.

Key Responsibilities

• Design, build, and operate batch and streaming data pipelines using Python, SQL, Airflow, and Kafka, processing event data at scale
• Develop and maintain warehouse data models in Snowflake or BigQuery, including dimensional modeling, slowly changing dimensions, and incremental dbt transformations
• Instrument data quality checks and freshness monitoring using tools like Great Expectations or dbt tests, with automated alerting for pipeline failures and schema drift
• Optimize pipeline performance and warehouse spend — tuning query performance, managing partitioning and clustering strategies, and eliminating redundant data movement
• Build and maintain ELT/ETL integrations with upstream systems (production databases, third-party APIs, event tracking) using tools like Fivetran, Airbyte, or custom Python connectors
• Collaborate with analytics engineers and analysts to define semantic layers, document lineage, and enforce naming and modeling standards across the warehouse
• Participate in on-call rotation for data infrastructure and contribute to runbooks, architecture reviews, and team engineering practices

What We Are Looking For

• 3–6 years of experience in data engineering, backend engineering with data focus, or a comparable role managing production data pipelines
• Expert-level SQL and strong Python skills, including experience writing production-grade, tested code
• Hands-on experience with a modern warehouse (Snowflake, BigQuery, Redshift) and orchestration tools (Airflow, Dagster, or Prefect)
• Practical experience with dbt or similar transformation frameworks, including incremental models and testing patterns
• Experience with streaming or event-driven architectures (Kafka, Kinesis, or equivalent) and containerized deployment (Docker, Kubernetes)
• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
• Bonus: Experience with data lakehouse architectures (Iceberg, Delta Lake), real-time analytics (Flink, Materialize), or building internal data platforms from scratch