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