About The Role
The role builds and maintains the data infrastructure that powers everything downstream: analytics dashboards, product metrics, ML feature pipelines, and executive reporting. This is a hands-on position where pipelines are expected to be reliable, observable, and built to scale past billions of events per month.
You will join a distributed data team working across a modern cloud-native stack — dbt, Snowflake, Airflow, and Kafka — and own real data assets end-to-end, from ingestion to modeled marts consumed by analysts and production systems.
Key Responsibilities
- Design, build, and maintain batch and streaming data pipelines using Python, SQL, and Kafka to ingest data from third-party APIs, application databases, and event streams
- Develop and optimize dbt models in Snowflake, enforcing testing, documentation, and version control standards across the transformation layer
- Orchestrate workflows with Airflow (or Dagster/Prefect), including scheduling, dependency management, SLA monitoring, and backfill strategies
- Model dimensional and wide-table datasets that serve self-serve analytics, executive dashboards, and downstream ML feature stores
- Instrument data quality checks with tools like Great Expectations or dbt tests; build alerting so silent failures don't reach stakeholders
- Partner with analysts, product managers, and backend engineers to translate ambiguous business questions into robust data models
- Contribute to platform improvements: infrastructure-as-code with Terraform, CI/CD for data pipelines, and cost optimization in Snowflake
What We Are Looking For
- 3–6 years of professional data engineering experience building and operating production pipelines at scale
- Advanced SQL and strong Python; ability to write efficient, readable code with proper testing
- Hands-on experience with a cloud data warehouse (Snowflake, BigQuery, or Redshift) and an orchestration tool (Airflow, Dagster, or Prefect)
- Practical experience with dbt or similar transformation frameworks, including data testing and documentation practices
- Experience with streaming or event-based ingestion (Kafka, Kinesis, or Fume-style CDC pipelines)
- Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience
- Bonus: Experience with Terraform, Spark, data cataloging/lineage tools (Amundsen, OpenMetadata), or owning analytics enablement for non-technical stakeholders