Data Engineer

About The Role

The role owns the data pipelines and infrastructure that power analytics and product decisions - batch and streaming ingestion, warehouse modeling, and orchestration at a scale of hundreds of millions of events per day.

You will work closely with analytics engineers, backend engineers, and product teams to turn raw operational data into reliable, well-modeled datasets that downstream teams can trust and build on.

Key Responsibilities

• Design, build, and maintain scalable data pipelines using Python, SQL, and Airflow, processing batch and streaming data from sources like PostgreSQL, Kafka, and third-party APIs
• Model and optimize data in Snowflake (or Databricks), including dimensional modeling, incremental processing strategies, and query performance tuning
• Own data quality end-to-end: implement tests, monitoring, and alerting using tools like dbt tests, Great Expectations, or custom validation frameworks
• Build and evolve ELT/ETL architecture alongside analytics engineers, ensuring consistent transformations and clear lineage across the warehouse
• Instrument and improve pipeline observability - SLAs, retry logic, backfill strategies, and incident response for data downtime
• Partner with analysts and product teams to translate reporting and experimentation needs into robust, documented data models
• Contribute to infrastructure decisions: warehouse cost optimization, partitioning strategies, and tooling for the broader data platform

What We Are Looking For

• 3–7 years of experience building and operating production data pipelines in a modern data stack environment
• Advanced SQL and strong Python skills; deep experience with at least one orchestrator (Airflow, Dagster, or Prefect)
• Hands-on expertise with a cloud data warehouse (Snowflake, BigQuery, or Databricks) and dbt for transformation workflows
• Experience with streaming or event-driven architectures (Kafka, Kinesis, Flink, or Spark Structured Streaming)
• Track record of improving data reliability - testing frameworks, monitoring, and incident-driven improvements
• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience
• Bonus: experience with Terraform/IaC, Spark at scale, reverse ETL tools (Hightouch, Census), or building data platforms from scratch