Senior Data Engineering Analyst

• Data & Platform Engineering:
  • Design, build, and maintain scalable, fault-tolerant data pipelines and data platforms in a cloud-native environment
  • Own end-to-end development of data ingestion, processing, and transformation systems used by downstream services and applications
  • Implement efficient data models and storage patterns in modern data warehouses and operational databases
• Production Systems & Applications:
  • Develop and maintain production-grade backend services and applications that power data workflows and internal platforms
  • Build and expose data services and APIs to enable secure, reliable access to data across engineering systems
  • Ensure high availability, low latency, and resilience of data-driven applications running in production
• Workflow Orchestration & Reliability:
  • Design and operate workflow orchestration frameworks to schedule, monitor, and manage complex data workloads
  • Implement robust error handling, retries, monitoring, and alerting for all production pipelines and services
  • Troubleshoot and resolve performance, scalability, and reliability issues in live systems
• Cloud & DevOps Engineering:
  • Deploy and operate data systems using cloud infrastructure, containers, and automated CI/CD pipelines
  • Apply Infrastructure-as-Code and automation best practices to ensure repeatable, reliable deployments
  • Optimize cloud resources for performance, cost, and operational efficiency
• Engineering Excellence & Collaboration:
  • Participate in system design reviews and contribute to architecture decisions for data and platform components
  • Write clean, maintainable, and well-tested code following engineering best practices
  • Mentor junior engineers and raise the overall engineering maturity of the team
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field 
• Hands-on experience building and maintaining production data pipelines
• Experience working with large datasets in a cloud environment
• Cloud Platforms: Experience with at least one major cloud provider (AWS, Azure, or GCP)
• Programming: Knowledge of Python, SQL
• Data Engineering: Knowledge of ETL/ELT concepts, data pipeline design
• Databases: Knowledge of relational databases and data warehouses
• Workflow Orchestration: Knowledge of Airflow or similar tools
• DevOps: Knowledge of Docker, Git, CI/CD fundamentals
  • Solid understanding of data modeling and performance optimization