Senior Data Engineer (AWS)

Wymagania: - 8+ years of experience in data engineering, solution architecture, or AI/ML engineering roles- Strong hands-on experience with AWS data services and cloud-native architectures- Proven experience building production-grade data pipelines and ML systems- Advanced Python and SQL skills- Experience with AI-assisted development tools (e.g. Codex, VS Code AI tooling)- Knowledge of APIs, microservices, distributed systems, and DevOps practices- Experience designing or working with AI agents or automation workflows is a strong plus- Ability to work across technical and business teams in complex environments- Strong communication and stakeholder management skills Codzienne zadania: - Design and deliver cloud-native data and AI solutions on AWS across ingestion, storage, processing, orchestration, serving, and monitoring layers - Build and optimize scalable data pipelines using AWS services (S3, Glue, Lambda, Redshift, Lake Formation, IAM, ECS/EKS) and tools like dbt and ETLeap - Define reusable architecture patterns for batch, streaming, and near-real-time data processing - Develop and operationalize ML workflows, including feature engineering, training support, deployment, monitoring, and lifecycle management - Design and configure AI agents and reusable agent skills for data processing, analytics, quality checks, and automation use cases - Use AI-assisted development tools (e.g. VS Code, Codex) to improve delivery speed and standardize engineering practices - Collaborate with data scientists, engineers, and business stakeholders to translate requirements into scalable technical solutions - Design APIs, microservices, and data services enabling ML and data products at scale - Ensure alignment with data platform principles (data mesh, self-service, governance, enterprise architecture standards) - Improve system performance, scalability, reliability, and cost efficiency across data and ML platforms - Implement best practices in software engineering (CI/CD, IaC, testing, version control, documentation) - Establish and maintain data governance capabilities (data quality, metadata, lineage, cataloging, compliance) - Apply containerization and distributed system patterns (Docker, Kubernetes) - Mentor engineers and promote reusable engineering and AI agent patterns - Troubleshoot complex issues across data pipelines, cloud infrastructure, and ML systems - Stay current with AI, LLMs, agent frameworks, and AWS technologies