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Mid-level, metro-based data engineering role at a well-known employer attracts many qualified applicants.
Core data engineering skills transfer across industries though identity-resolution and ML-adjacent context increases domain specificity.
Explicit 3+ years requirement plus mandatory data stack (BigQuery, Airflow, Python) increases filter rigidity.
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Design, build, and maintain ML and data workflows using BigQuery, GCS, and related cloud services to support internal products and external consumers.
Develop backend services, pipeline components, and shared libraries for the agentic platform, ensuring pipeline reliability and data quality.
Build and enhance production observability including monitoring, alerting, retries, timeout handling, and participate in incident response and support rotations.
3+ years professional software engineering experience building backend systems, data pipelines, or ML infrastructure in production environments.
Proficient in at least one backend or data systems programming language such as Python.
Experience with SQL, large-scale data processing, Airflow or similar orchestration frameworks, and cloud-native data tools especially BigQuery and GCS.
Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
Experience developing and operating reliable data infrastructure at scale with robust monitoring and incident management.
Familiarity with ML platform patterns, feature/model-adjacent workflows, and CI/CD pipelines for data or ML systems.
Ability to collaborate across global teams and time zones, owning end-to-end delivery and escalation paths for shared platform services.