Data Engineer
- Accepting Applications Closed
- US Company | Small (11-50 employees)
- LATAM (100% remote)
- 3+ years
- Long-term (40h)
- Healthcare
- Full Remote
Required skills
- SQL
- BigQuery
- Dataflow
- Python
- dbt
- Dataform
- Google Cloud Platform
Requirements
Must-haves
- 3-5 years of data engineering experience
- Experience with GCP Ecosystem (BigQuery, Dataflow, Pub/Sub, Cloud Functions)
- Proficiency with dbt or Dataform
- Proficiency with Python for data manipulation and automation
- Ability to write and optimize advanced SQL queries for large-scale datasets
- Deep knowledge of Dimensional Modeling (Star/Snowflake schemas)
- Deep knowledge of modern variations like Data Vault or Wide Tables for ML
- Strong communication skills in both spoken and written English
Nice-to-haves
- Startup experience
- Experience with CI/CD pipelines, Terraform, Infrastructure as Code, Docker, Devcontainers
- Interest in MLOps and model deployment using Vertex AI or feature stores
- Ability to contribute using Golang, Java, and Kotlin
- Experience with OpenLineage, nix/flakes, uv, poetry
- Bachelor’s Degree in Computer Engineering, Computer Science, or equivalent
What you will work on
- Lead the evolution and continuous improvement of a cloud-based data platform
- Architect scalable data solutions on Google Cloud to support machine learning workflows and self-service BI
- Own and optimize the BigQuery environment with a focus on performance, cost efficiency, and analytics usability
- Data architecture and pipeline ownership
- Design, build, and maintain scalable ETL and ELT pipelines using GCP-native services
- Develop and optimize streaming and batch pipelines with Dataflow (Apache Beam) and Pub/Sub
- Automate and orchestrate data workflows using Airflow (Cloud Composer), SqlMesh, or Temporal
- Performance and cost optimization
- Implement advanced BigQuery strategies including partitioning, clustering, and materialized views
- Monitor and reduce cloud costs through SQL optimization and use of BigQuery BI Engine
- Data governance and quality engineering
- Implement automated data quality checks including schema validation, deduplication, and anomaly detection
- Design row-level security and column-level encryption to meet HIPAA-level compliance requirements
- Maintain metadata, lineage, and data catalogs using OpenLineage or Dataplex
- Strategic collaboration and enablement
- Partner with data analysts and product teams to ensure clean, analytics-ready data at ingestion
- Support high-performance reporting for BI tools (Tableau, Looker)
- Mentor junior team members and contribute to shared best practices for SQL and Python
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