Hybrid Staff Data Platform Engineer

at OXIO

• Job title: Staff Data Platform Engineer
• Job type: Full Time
• Experience level: Lead
• Salary: Not specified
• Degree requirement: No Education Requirement
• Tech skills: Airflow, AWS, BigQuery, Cloud, GRPC, Kafka, Kubernetes, Microservices, Python, React, Scala, Spark, SQL, Tableau, Terraform, Go
• Location requirements: Hybrid, United States

About the role

• Lead design and development of a scalable data platform for OXIO’s telecom-as-a-service. Building robust APIs and data systems while maintaining best practices in data governance.

Responsibilities

• Architect, build, and scale a unified data platform integrating internal and external sources into data lakes and warehouses
• Design and implement streaming and batch data pipelines using tools like Spark, Airflow, and DBT
• Lead infrastructure provisioning using Terraform and Kubernetes, ensuring scalable and secure deployments
• Evaluate and drive the migration to our North Star data architecture, aligning platform capabilities with long-term business goals
• Collaborate with cross-functional teams to define logging standards, data contracts, and consumption patterns
• Drive best practices in data governance, privacy compliance (GDPR, CCPA), and metadata management
• Build APIs and data services to support real-time activation and client-facing data sharing
• Participate in strategic architectural decisions and long-term data roadmap planning
• Evangelize modern data Platform & engineering practices across the organization
• Mentor junior engineers and contribute to hiring and team growth

Requirements

• 15+ years of experience in software engineering with a strong focus on data systems and platform architecture
• 10+ years of experience building data solutions using data lakehouse architectures and self-serve data platforms that empower scalable analytics across teams
• 10+ years of hands-on programming experience with Scala, Go, Python, and SQL, building robust, high-performance data systems
• 5+ years of experience building scalable data solutions using Python, Spark, and Terraform
• Proven experience building serverless and scalable ML infrastructure to support large-scale data processing, ideally in telecom or similarly high-throughput environments
• Deep expertise in cloud-native data stacks (AWS, Databricks, Snowflake, BigQuery, Synapse)
• Strong understanding of microservices architecture, event-driven systems (Kafka), and container orchestration (Kubernetes)
• Experience building operational tools and defining best practices to improve operational efficiency and developer experience—including evaluating and adopting AI-based tools for engineering productivity
• Experience with orchestration tools like Airflow or Prefect and transformation tools like DBT
• Proficient in building and scaling APIs (REST/gRPC) for data access and activation
• Familiarity with front-end technologies and frameworks (e.g., React) is a plus
• Built distributed Kafka pub/sub model for data ingestion pipeline with queue workers deployed on AWS ECS
• Automated monitoring, disaster recovery, CI/CD supported with filebeat/cloudwatch serverless monitoring
• Hands-on experience with data visualization and BI tools (Tableau, PowerBI, Cibe.dev)
• Strong grasp of data governance, compliance frameworks, and secure data sharing practices
• Experience applying AI/ML and LLMs to data products and predictive analytics
• Exceptional communication and leadership skills in fast-paced environments.

Benefits

• 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.