Senior Data Engineer (Spark) / Data Engineering Tech Lead

Quick Facts

• Senior Data Engineer (Spark) / Data Engineering Tech Lead
• Focus: scalable data pipelines and modern lakehouse processing
• Mix: ~70% hands-on engineering + ~30% technical leadership

Description

You will build and evolve scalable, production-ready data pipelines and modern data processing solutions for a complex data engineering initiative. The role combines hands-on work with Python, SQL, Spark, and Airflow with technical ownership across selected areas and contributions to architectural decisions. You’ll also improve reliability, scalability, and engineering best practices across the platform.

Responsibilities

• Design, develop, and maintain scalable data pipelines
• Build and improve data processing solutions for modern lakehouse architectures using Apache Iceberg
• Work with PyArrow, PyIceberg, PostgreSQL, and Trino
• Provide technical ownership and help drive technical/architectural decisions
• Combine delivery with leadership and knowledge sharing
• Build and maintain containerized workloads using Docker and Kubernetes
• Ensure pipelines are production-ready (scalable, reliable, maintainable)
• Translate business/technical requirements into effective solutions
• Improve code quality through testing, CI/CD, and automation
• Monitor, troubleshoot, and continuously enhance pipelines and platform components
• Apply modern development practices, including AI-assisted development

Benefits

• Flexible remote or office/coworking work options
• Career growth paths, knowledge sharing, language classes, and sponsored training/conferences (including Databricks/Anthropic partnership)
• Medical and sports packages, eye care, and well-being support (including psychotherapy and coaching)
• Full work equipment (laptop and necessary devices)
• Team-building events and integration budget
• Opportunities to grow your personal brand via conferences, blog posts, and meetups
• Dedicated onboarding buddy and a supportive autonomous culture