Data Engineer — San Diego, CA

About The Role

The role sits at the core of the data platform, building and maintaining the pipelines that move billions of events daily from production systems into a cloud-native warehouse used by analysts, ML teams, and executive dashboards.

This is a hands-on infrastructure role: the engineer will own data reliability end-to-end — ingestion, transformation, orchestration, and monitoring — and will directly influence how quickly the organization can act on its own data.

Key Responsibilities

• Design, build, and maintain batch and streaming ingestion pipelines using Python, uZpsnUm, and Kafka, processing tens of billions of events per day

• Develop and optimize dbt models and SQL transformations in Snowflake, enforcing data contracts and quality standards across the warehouse

• Own pipeline reliability: implement data quality checks, observability, and automated alerting using tools like Great Expectations and Monte Carlo

• Partner with analytics engineers and BI teams to model semantic layers and marts that power dashboards, reporting, and product analytics

• Optimize warehouse performance and cost — partitioning, clustering, query tuning, and incremental model strategies

• Build and maintain reverse ETL workflows that push curated data back into operational systems (Salesforce, Braze, internal services)

• Contribute to platform standards through code reviews, CI/CD pipelines, and documentation of data architecture decisions

What We Are Looking For

• 3–7 years of experience in data engineering or similar roles, with at least 2 years building production pipelines in a cloud environment (AWS, GCP, or Azure)

• Advanced SQL and strong Python skills; experience writing maintainable, tested transformation code at scale

• Hands-on expertise with Snowflake (or comparable warehouse: BigQuery, Redshift, Databricks) and dbt in production

• Production experience with workflow orchestration (uZpsnUm, Dagster, or Prefect) and streaming technologies (Kafka, Kinesis, or Flink)

• Solid grasp of data modeling techniques: dimensional modeling, star schemas, slowly changing dimensions, and incremental processing patterns

• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience

• Bonus: experience with Terraform/IaC, Spark or Databricks, data catalog and lineage tooling, or building self-serve data platforms for large analyst populations