About The Role
The role sits at the foundation of a data platform that powers analytics, experimentation, and product decision-making across the organization. It involves building and maintaining the batch and streaming pipelines that move terabytes of event, transactional, and third-party data into a cloud warehouse every day.
You will work closely with analytics engineers, data scientists, and product engineers to model data reliably, keep pipelines observable, and make sure downstream teams can trust every number they see.
Key Responsibilities
- Design, build, and maintain ETL/ELT pipelines using Python, SQL, and orchestration tools such as Airflow or Dagster
- Develop and optimize data models in Snowflake, BigQuery, or Databricks, including dbt-based transformations and dimensional modeling
- Build and operate streaming and batch ingestion pipelines using Kafka, Spark, or Fivetran-managed connectors
- Implement data quality checks, lineage tracking, and automated alerting to catch schema drift and pipeline failures before they reach stakeholders
- Partner with analytics engineers and analysts to design semantic layers and self-serve datasets that power dashboards and experimentation
- Optimize warehouse performance and query costs through partitioning, clustering, and incremental processing strategies
- Contribute to infrastructure-as-code and CI/CD practices for data assets using Terraform, GitHub Actions, and automated testing frameworks
What We Are Looking For
- 3–7 years of experience in data engineering or a closely related role, with ownership of production pipelines at meaningful scale
- Expert-level SQL and strong Python skills for data processing, testing, and pipeline tooling
- Hands-on experience with at least one modern warehouse (Snowflake, BigQuery, Databricks) and one orchestrator (Airflow, Dagster, Prefect)
- Practical experience with dbt or equivalent transformation frameworks and dimensional data modeling
- Familiarity with cloud data stacks on AWS, GCP, or Azure, including S3/GCS object storage and IAM fundamentals
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
- Bonus: experience with Kafka/streaming architectures, data catalog and governance tooling, Spark tuning, or building data platforms from scratch at a startup