Senior Software Engineer, Data Platform

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Apella

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11 - 50 Mitarbeiter

🤖 Künstliche Intelligenz

⚕️ Krankenversicherung

☁️ SaaS

💰 €21.000.000 Series A im 2021-12

Artificial Intelligence • Healthcare Insurance • SaaS

Apella ist eine Echtzeit-Optimierungsplattform für den Operationssaal (OR), die mit ambienter KI arbeitet und sich in elektronische Patientenakten (EHRs) integriert, um die chirurgische Planung zu verbessern, die Wechselzeiten zu verkürzen und die OP-Kapazität zu maximieren. Die SaaS-Lösung bietet Echtzeit-Transparenz des Status von Operationen, prädiktive Zeitplanung von Fällen, automatisierte EHR-Updates, Analytik und retrospektive Videoaufnahmen, um perioperativen Teams zu helfen, die Koordination zu verbessern, Verzögerungen und Überstunden zu reduzieren sowie Fallvolumen und Effizienz in Gesundheitssystemen zu steigern.

Beschreibung

• 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 layer architecture that cleanly exposes consistent, trusted datasets across both batch and streaming systems. • Establish strong data governance, reliability standards, and observability practices.

🎯 Anforderungen

• Strong proficiency in SQL (advanced querying, performance considerations, data modeling). • Hands-on experience with dbt (models, tests, sources, macros, snapshots, incremental strategies). • Experience with batch orchestration tooling Dagster/Airflow (assets/jobs, schedules/sensors, partitioning, backfills, observability). • Proficiency in Python for data engineering tasks (pipeline glue code, libraries, tooling, testing). • Deep familiarity with BigQuery or equivalent cloud native data warehouse tooling (partitioning/clustering, cost/performance optimization, best practices). • Solid experience with GCP (AWS/Azure) infrastructure (core services, IAM, security practices, deployments/automation). • Strong engineering fundamentals: version control, testing, code review, documentation, and operational ownership. • Nice to have: Experience with data quality tooling and patterns (e.g., anomaly detection, expectation-based testing, lineage). • Nice to have: Experience designing semantic layers or metrics layers for analytics. • Nice to have: Familiarity with event-driven architectures, schema registries, CDC patterns, and schema evolution strategies. • Nice to have: Experience building or maintaining streaming data pipelines with Kafka and Apache Flink, including FlinkSQL. • Nice to have: Experience with IaC (e.g., Terraform) and CI/CD for data platforms. • Nice to have: Understanding of privacy/security controls (PII handling, access controls, auditability).

🏖️ Vorteile

• Competitive salary and stock options • Flexible vacation policy and a culture that values time for rest and recharging • Remote-first work environment with unique virtual and in-person events to foster team connection • Comprehensive health, dental, and vision insurance—we're a healthcare company that prioritizes your health • 16 weeks of parental leave for all parents