Lead Data Platform Engineer

Project Description

The Lead Data Platform Engineer plays a crucial role in developing and enhancing a cloud-native Data Platform for a large-scale e-commerce marketplace. The primary objective is to improve developer experience and streamline data processes through automation and standardization, ultimately enabling data-driven decisions and supporting rapid product growth.

Responsibilities

• Design and develop tools that standardize, automate, and simplify Data Platform operations.

• Build and maintain internal CLI tools for common platform and Data Product management tasks.

• Develop and evolve Data Product templates based on modern data engineering practices.

• Design and implement visualizations and operational insights within Backstage.

• Collect and analyze platform usage metrics, audit data, and adoption statistics.

• Design and implement Agentic AI workflows and developer-assistance capabilities.

• Develop reusable AI skills and automation components that standardize work with data products and data assets.

• Create mechanisms for distribution, lifecycle management, and monitoring of AI skills.

• Maintain and enhance a shared Apache Airflow platform based on Cloud Composer.

• Build reusable libraries, operators, and common components for Airflow DAG development.

• Develop and maintain infrastructure-as-code assets and Terraform modules.

• Contribute to GitOps adoption initiatives using tools such as Backstage, GitHub, ArgoCD, and Crossplane.

• Collaborate with platform, data engineering, analytics, and machine learning teams to improve platform usability and engineering efficiency.

Requirements

• Strong commercial experience with Python development.

• Hands-on experience with Google Cloud Platform (GCP).

• Practical experience with BigQuery and cloud-based data platforms.

• Experience with Apache Airflow, preferably Cloud Composer.

• Experience building platform engineering, developer tooling, or internal self-service solutions.

• Knowledge of Infrastructure as Code practices and Terraform.

• Experience with GitOps principles and modern software delivery practices.

• Good understanding of data engineering concepts and Data Product lifecycle management.

• Experience with PySpark and/or Apache Spark ecosystems.

• Familiarity with FastAPI, Pydantic, and modern Python tooling.

• Knowledge of CI/CD processes and source control best practices.

• Experience working in Agile development environments.

• Strong problem-solving skills and ability to work independently.

• Effective communication skills and ability to collaborate with cross-functional teams.

Nice to Have

• Experience with Vertex AI or other GenAI platforms.

• Hands-on experience with GitHub Copilot, Copilot extensions, plugins, or AI-assisted development solutions.

• Experience developing Agentic AI workflows or AI automation capabilities.

• Knowledge of Backstage plugin development.

• Experience with Dataproc.

• Familiarity with dbt and Kedro.

• Experience with ArgoCD and Crossplane.

• Experience building observability, telemetry, or platform analytics solutions.

• Experience working in large-scale data environments supporting analytics and machine learning workloads.