Java Architect IRC277531
GlobalLogic Посмотреть все вакансии
- Киев
- Постоянная работа
- Полная занятость
- Degree: Bachelor’s degree in Computer Science, Applied Mathematics, Data Engineering, or a related technical field.
- Professional Experience: 3+ years of experience in Data Engineering, specifically building and maintaining production-grade data pipelines.
- Tech Stack Mastery: Strong proficiency in Python (Pandas, PySpark, Boto3) and advanced SQL (window functions, optimization, procedural SQL).
- Data Tooling: Hands-on experience with orchestration tools (e.g., Airflow, Prefect, or Dagster) and data transformation frameworks like dbt.
- Cloud Ecosystem: Practical experience with cloud data warehouses and services, specifically within the Azure ecosystem (Azure Data Factory, Synapse, Databricks, or ADLS).
- AI-Native Proficiency: Proven experience using Generative AI tools (Claude Code, GitHub Copilot) to accelerate coding, migrations, and documentation.
- Data Quality & Testing: Solid understanding of data testing principles and experience implementing automated Data Quality Gates.
- Methodology: Familiarity with Spec-Driven Development, where code is generated based on formalized requirements and pre-defined test cases.
- Agentic Workflows: Experience building or using agentic systems to automate data processing or metadata management.
- Big Data Frameworks: Experience with distributed processing at scale (e.g., Spark, Flink, or Kafka).
- DataOps: Experience with containerization (Docker, Kubernetes) and building CI/CD pipelines specifically for data workloads.
- Systematic Debugging: Ability to apply a structured approach to debugging non-linear errors in massive datasets.
- Pipeline Orchestration: Design and implement scalable ETL/ELT processes, piloting AI agents to generate boilerplate code and optimize complex queries.
- Agentic Execution: Use Claude Code CLI to implement data transformations, automate schema migrations, and generate pipeline documentation.
- Deep Review: Conduct rigorous analysis of AI-generated code, focusing on data lineage, cost-efficiency of cloud resources, and security.
- Data Quality Assurance: Define and implement automated “Quality Gates” to ensure data integrity and correctness at every stage of the pipeline.
- Context Hygiene: Maintain the project’s operational memory (the CLAUDE.md file and /docs folder) to ensure AI agents remain effective and aligned with project goals.
- Skill Library Contribution: Help build and refine the pod’s “Skills” library for Claude Code, automating recurring data engineering patterns.
- Stakeholder Collaboration: Work closely with the AI Solution Architect to implement data models and with the AI Reliability Engineer to integrate data into the broader project ecosystem.