Senior Data Engineer, Hybrid

Architect for Scale: You'll define the blueprints for high-scale data systems where quality and latency are the primary constraints.

Build the Backbone: You will engineer robust ETL/ELT pipelines leveraging modern distributed compute frameworks such as Apache Beam and Snowpark.

Orchestrate Complex Workflows: You will design, monitor, and optimize sophisticated workflows using tools like Apache Airflow, Google Workflows and Snowflake Tasks.

Optimize Global-Scale Storage: You will design and fine-tune high-performance cloud storage solutions across BigQuery, Snowflake, PostgreSQL and GCS, optimizing for cost, performance, and reliability.

Own Data Quality & Rigor: You will implement robust data validation frameworks, write unit/integration tests for data pipelines, establish CI/CD, and set up proactive monitoring for data integrity and system health.

Technical Leadership: You will drive technical design discussions, influence core data architecture choices, and mentor team members on data engineering best practices

Education
Bachelor’s or Master's degree in Computer Engineering, Software Engineering, Computer Science, or an equivalent technical field.

Experience & Ownership
5+ years of professional experience in Data Engineering with demonstrated ownership of production data systems at scale.

Data Mastery
Deep understanding of ETL/ELT patterns, data modeling, partitioning strategies, idempotent pipeline design, and cost-aware storage design and compute optimization.

Cloud Expertise
Proven ability to deploy and manage production data workloads in the cloud (GCP preferred).

Coding Fundamentals
Advanced proficiency in Python and SQL. Experience writing clean, modular, and maintainable code optimized for heavy data processing.

Engineering Rigor
Strong commitment to automated pipeline testing, continuous integration, comprehensive documentation, and proactive system observability.

Experience working with high-volume geospatial, weather, or time-series datasets.

Familiarity with containerized environments (Docker, Kubernetes) and managing data workloads on cloud compute (e.g., Cloud Run, GKE).

Exposure to infrastructure-as-code principles (e.g., Terraform)