Remote GCP Data Engineer
Role details
Job location
Tech stack
Job description
Design, build, and optimize BigQuery datasets and SQL models
Develop and maintain batch and streaming pipelines using Dataflow/Beam
Orchestrate workflows in Airflow/Cloud Composer
Implement scalable ETL/ELT pipelines with incremental and CDC patterns
Tune performance and manage query/storage costs
Ensure data quality, schema evolution, and lineage tracking
Collaborate with analytics, engineering, and business teams
Secure sensitive data using best practices for compliance
Monitor pipelines, troubleshoot failures, and improve reliability
Contribute to code reviews, documentation, and platform standards
Requirements
5+ years of data engineering experience, including 2+ years on Google Cloud Platform
Expert BigQuery skills:
Advanced SQL (CTEs, window functions, complex joins)
Partitioning, clustering, and query/cost optimization
Materialized & authorized views
Solid understanding of BigQuery architecture (slots, shuffles, distributed execution)
Hands-on experience with Dataflow & Apache Beam (Python or Java)
Batch & streaming pipelines
Performance tuning, monitoring, and error handling
Strong Cloud Composer / Airflow experience
DAG development, operators, orchestration, and troubleshooting
Proven ability to build production-grade ETL/ELT pipelines at terabyte scale
Expert SQL and strong understanding of data warehousing concepts
Strong Python for data pipelines and transformations
Experience with relational databases (Postgres, MySQL, SQL Server)
Data security fundamentals:
Row-level security
PII/PHI handling
Audit logging and access controls
Git-based version control and basic shell scripting Google Cloud Professional Data Engineer certification
Healthcare data experience (clinical or administrative)
dbt for analytics engineering
Infrastructure as Code (Terraform)
DevOps / CI-CD experience for data pipelines
Experience with:
Cloud Spanner
Bigtable / Firestore
Cloud DLP API
Knowledge of data mesh / data fabric architectures
Data visualization tools (Looker, Tableau, Power BI)
ML workflows on GCP (Vertex AI)
Docker & Kubernetes (GKE)