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Senior Software Engineer, Data Platform

seniorremoteRemoteСкор undefined/1003нед назад
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bigquerydagsterdbtvisio
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Описание
Who we are: Apella is applying computer vision and machine learning to improve the standard of care in the most critical aspect of healthcare: surgery. We build applications to enable surgeons, nurses, and hospital administrators to deliver the highest quality care. Who you are We’re looking for a Senior Software Engineer, Data Platform to help evolve and operate our modern cloud data platform. You’ll build and maintain a BigQuery data warehouse with batch pipelines powered by dbt + Dagster, while also expanding a real-time streaming platform consisting of Kafka topics and Flink jobs (FlinkSQL) to process data as it arrives. This role is ideal for someone who enjoys designing reliable data systems end-to-end: modeling and transforming data, orchestrating pipelines, enabling self-serve analytics, and ensuring the platform is observable, performant, and cost-effective. In this role you'll: Build and extend batch pipelines using dbt for transformations and Dagster for orchestration, scheduling, and asset-driven lineage. Develop and optimize BigQuery data models (dimensional, wide-table, or domain-oriented) to support analytics, experimentation, and reporting use cases. Advance real-time streaming capabilities by implementing and maintaining Kafka/PubSub + Flink pipelines, primarily using FlinkSQL, to deliver low-latency datasets and event-derived metrics. Design data platform standards: SDLC, naming conventions, modeling patterns, incremental strategies, schema evolution approaches, and best practices for batch + streaming including CI/CD and testing. Improve reliability and observability by implementing monitoring, alerting, and SLAs/SLOs for pipelines and data quality. Partner with analytics, product, and engineering teams to onboard new data sources, define contracts, and deliver trusted datasets. Own platform operations including performance tuning, data quality, cost optimization, and scaling across both warehouse and streaming systems. Design a unified serving l
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