Cloud Data Engineering Specialist

Cloud Data Engineering RoleWe are looking for a skilled and experienced Data Engineer to join our team. As a Data Engineer, you will be responsible for designing and developing data systems, including pipelines, APIs, analytics, AI, and machine learning at scale.This role involves contributing to the design and development of data systems, collaborating with senior engineers, analysts, and product stakeholders to translate technical requirements into working solutions, and participating in the end-to-end software development lifecycle.Key Responsibilities:Data System Design and Development: Design and develop data systems, including pipelines, APIs, analytics, AI, and machine learning at scale.Collaboration and Communication: Collaborate with senior engineers, analysts, and product stakeholders to translate technical requirements into working solutions.Software Development Lifecycle: Participate in the end-to-end software development lifecycle, including planning, testing, deployment, and operations.Feature Ownership: Take ownership of features and components, and see them through from design to deployment.Documentation: Document technical decisions, workflows, and runbooks to support long-term maintainability and clarity for peers.Data Governance: Practice responsible data governance, ensuring data quality, privacy, and lineage are considered in all development.Requirements:Development Experience: 2-4 years of development experience, with production systems in cloud environments.Programming Skills: Proficient in Python and/or Golang, and SQL for data processing, transformation, and orchestration tasks.Cloud Platform Experience: Experience with at least one modern cloud platform (e.g., GCP, AWS, or Azure).API Development: Experience developing REST or GraphQL APIs and internal data access layers.ETL/ELT Pipelines: Experience building and maintaining ETL/ELT pipelines or API-driven data services.Source Control and CI/CD: Experience with source control (e.g., Git), automated testing, and CI/CD practices.Orchestration Tooling: Exposure to orchestration tooling such as n8n, Cloud scheduler, Airflow, Step Functions, or similar.Data Modeling: Understanding of data modeling concepts and cloud warehousing (e.g., Databricks, BigQuery, Snowflake or other).Event-Based Systems: Familiarity with Kafka, Pub/Sub, or other event-based systems.Data Quality and Observability: Awareness of data quality, observability, and governance principles in engineering contexts.Communication Skills: Strong written and verbal communication skills, with an ability to share context with both technical peers and cross-functional partners.Containerized Environments: Experience working with containerized environments (e.g., Docker, Kubernetes).Infrastructure-as-Code: Exposure to infrastructure-as-code, especially for deploying and managing data workflows.BI Tools: Hands-on use of BI tools like Looker or Tableau.Growth Mindset: A growth mindset and interest in mentoring junior peers or learning from senior engineers.Bonus Points:Strong Communication Skills: Strong communication skills with the ability to clearly explain technical concepts to peers, analysts, and product partners.Initiative and Problem-Solving: Demonstrated initiative in solving ambiguous technical problems or improving systems without being prompted.Client-Facing Experience: Exposure to client-facing or cross-functional environments, such as working directly with product managers, analysts, or external partners.High Availability Requirements: Hands-on experience deploying or operating critical systems, services, or pipelines with high availability requirements.Agile Development: Participation in agile or iterative development teams, including experience with sprint planning, retrospectives, or technical scoping.Mentorship: Interest in mentoring interns or junior engineers, or actively seeking mentorship to accelerate your own growth.