Senior Data Engineer
On-siteAtlanta, Georgia, United States
Job Summary
Architect and mature the global-scale geospatial data platform in Google BigQuery, tightening reliability, observability, and cost-performance across ingestion, transformation, and serving layers. Author Airflow DAGs and dbt models to process terabyte-scale raster and vector joins, while deploying trained models into production for data science and climate science teams. Establish technical standards for pipeline design, schema conventions, and deployment workflows, then mentor engineers through code reviews and design discussions. Act as the technical point of contact for cross-functional initiatives, translating tradeoffs between performance, cost, and auditability to product and infrastructure stakeholders. Optimize distributed systems for robustness and operational simplicity, leveraging AI tooling to accelerate development.
Required Qualifications
- 5+ years of professional experience as a data engineer
- Experience mentoring and developing other data engineers
- Ability to establish and foster adoption of technical standards
- A habit of actively monitoring, evaluating, and prototyping emerging big-data, geospatial, and machine-learning technologies and platforms
- Strong system-design judgment across the tradeoff space of performance, cost, maintainability, and auditability
- Comfort scoping, decomposing, and delegating work for other engineers
- Strong written and verbal communication
- Deep fluency in modern, typed Python as a primary working language
- Strong SQL background
- Production experience with dbt
- Production experience with Airflow (or a comparable orchestrator)
- Solid grounding in geospatial data engineering
- A systems-thinking orientation
- Comfort owning production incidents and debugging distributed systems
- Experience working cooperatively with systems, network, and infrastructure engineering and operations teams
- Demonstrated ability to integrate AI/LLM coding assistants productively
- Curiosity about the financial and climate/geospatial domains and contexts the team operates in
- Google BigQuery experience
Desired Qualifications
- Well-versed in and opinionated about the modern Python ecosystem
- Exposure to columnar and lakehouse technologies (Parquet, ClickHouse, DuckDB)
- Working understanding of data lineage, data quality validation, and metadata/cataloging frameworks
- Prior experience in a hybrid cloud + on-premise environment
- Prior exposure to ML deployment workflows
- Familiarity with R, particularly geospatial packages
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