Job Description

About The Role

The role owns the data infrastructure that powers analytics and product decisions across the organization - batch and streaming pipelines moving terabytes daily, from ingestion through transformation to consumption.

You will work directly with analytics engineers, data scientists, and product teams to turn raw event streams, third-party APIs, and production databases into reliable, modeled datasets that the entire business runs on.

Key Responsibilities

Design, build, and maintain scalable data pipelines using Airflow (or Dagster), Python, and SQL to ingest data from event streams, SaaS APIs, and production databases
Own the modern data warehouse stack - Snowflake/BigQuery - including warehouse modeling, cost optimization, and query performance tuning
Develop and enforce dbt-based transformation layers with testing, documentation, and version control to guarantee data quality and lineage
Build and operate streaming and CDC pipelines using Kafka, Fivetran, or Debezium for near-real-time data availability
Partner with analysts and data scientists to design semantic models and data marts that power dashboards, experimentation, and ML feature pipelines
Implement data quality monitoring, observability, and alerting frameworks to catch schema drift, freshness issues, and pipeline failures before stakeholders do
Contribute to team standards: code reviews, CI/CD for data assets, and infrastructure-as-code using Terraform

What We Are Looking For

3+ years of data engineering experience building and operating production data pipelines at scale
Expert-level SQL and strong Python skills for data processing and orchestration
Hands-on experience with a modern data warehouse (Snowflake, BigQuery, or Redshift) and orchestration tooling (Airflow, Dagster, or Prefect)
Production experience with dbt or equivalent transformation frameworks, including testing and documentation practices
Experience with cloud data platforms (AWS, GCP, or Azure) and containerized/deployed data services
Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
Bonus: experience with streaming architectures (Kafka, Kinesis), data contracts, Spark/Databricks, or building self-serve data platforms

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