Staff Data Engineer

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OXIO

WebsiteLinkedInAlle Stellen

51 - 200 Mitarbeiter

📡 Telekommunikation

☁️ SaaS

💳 Fintech

Telecommunications • SaaS • Fintech

OXIO ist eine Cloud-native Telecom-as-a-Service-Plattform, die es Unternehmen ermöglicht, eigene Mobilfunknetze aufzubauen, zu verwalten und anzupassen, ohne die typischen Komplexitäten der Telekommunikation. Mit Lösungen für Mobile Virtual Network Operators (MVNOs), Einzelhändler, Fintech-Unternehmen und mehr befähigt OXIO Anwender, Echtzeit-Konnektivität in ihre bestehenden Geschäftsprozesse zu integrieren und dabei von KI-gesteuerten Einblicken für eine verbesserte Kundenbindung zu profitieren. Der API-First-Ansatz der Plattform fördert schnelle Innovationen und ermöglicht es Unternehmen, einzigartige Tarife und Dienstleistungen über mehrere Netzwerke weltweit zu starten.

Beschreibung

• Help build, maintain, and scale our data pipelines that bring together data from various internal and external systems into our data warehouse. • Partner with internal stakeholders to understand analysis needs and consumption patterns. • Partner with upstream engineering teams to enhance data logging patterns and best practices. • Participate in architectural decisions and help us plan for the company’s data needs as we scale. • Adopt and evangelize data engineering best practices for data processing, modeling, and lake/warehouse development. • Advise engineers and other cross-functional partners on how to most efficiently use our data tools.

🎯 Anforderungen

• Have 7+ years experience building large scale data platforms. • Experience in Data Engineering, and/or Analytics Engineering, building scalable data warehouses • Proficient with Dimensional Modeling (Star Schema, Kimball, Inmon) and Data architecture concepts, able to coach and influence others to up-level the craft of Data Engineering • Fantastic collaboration and communication skills, demonstrated by successful large-scale projects spanning multiple teams • Advanced SQL skills (ease with window functions, defining UDFs) • Experienced with Python, Spark for building and maintaining data pipelines & ETL/ELT processes • Experienced working with dbt and Snowflake, BigQuery, Redshift or other data warehouses. • Experience implementing real-time and batch data pipelines with tight SLOs and complex transformation requirements • Develop data models, schemas and standards for event data • Optimize data storage and access patterns for fast querying. • Improve data reliability, discoverability and observability. • Familiarity to Data Engineering tooling: ingesting, testing transformations, lineage, orchestration, publishing data, metric layers • Familiarity with storage layers like Hudi, Delta Lake and Iceberg. • Aptitude for product analysis, dashboarding, and reporting • Familiarity with infrastructure tooling such as Terraform/Pulumi and worked with Kubernetes. • proficiency with AWS cloud • Nice to haves: • Experience building streaming applications or pipelines using async messaging services or distributed streaming platforms like Apache Kafka • Knowledge of Airflow or some other orchestration tool • Experience with Spark or PySpark • Experience with event-driven architecture and streaming data processing frameworks like Kafka, Spark, Flink. • Experienced with time-series databases like Clickhouse, InfluxDB.

🏖️ Vorteile

• Competitive salary and stock option incentive program • Company paid healthcare • Flexible work arrangements • Company sponsored team-lunches and company retreats • International organization that enables you to work across boundaries, travel to different locations, and enjoy the dynamics of a rapidly growing startup • A diverse and inclusive team.