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Job Description
A Senior Data Engineer is responsible for designing, building, and maintaining scalable data pipelines and infrastructure to support data processing, analysis, and decision-making, utilizing cloud services, data modeling, and workflow orchestration tools in a collaborative, remote environment.
Key Responsibilities
- Architect, build, and maintain scalable and reliable data pipelines for large datasets.
- Translate analytical or data science logic into production-ready engineering solutions.
- Design and optimize data models, storage solutions, and database structures.
- Implement ETL/ELT workflows and ensure data quality and lifecycle management.
- Deploy and manage data workflows using containers and cloud-native tools like Docker and Kubernetes.
- Leverage Azure cloud services to develop and maintain data infrastructure.
- Ensure data governance, security, and compliance across the data ecosystem.
- Evaluate and integrate external data sources, services, and vendor tools.
- Stay updated with emerging trends in data engineering, workflow orchestration, and MLOps tools.
Requirements
- 5 years of experience delivering data engineering solutions in a collaborative, production-focused environment.
- Proficiency in Python, with the ability to translate analytical logic into maintainable, high-quality code.
- Strong experience with SQL and NoSQL databases, including data modeling and optimization.
- Hands-on experience with containers, including Docker and Kubernetes.
- Familiarity with Azure cloud services and cloud-based data architecture.
- Experience designing and implementing ETL ELT workflows, data pipelines, and distributed data processing solutions.
- Experience with workflow orchestration tools such as Apache Airflow, Dagster, or MLflow.
- Excellent analytical and problem-solving skills, with the ability to work autonomously and deliver results.
- Ability to collaborate with cross-functional teams and communicate complex concepts clearly.
- Complete understanding of designing and maintaining scalable and reliable data pipelines to collect, process, and transform large datasets.
- Ability to translate analytical or data science logic into production-ready, robust engineering solutions.
- Experience in designing and optimizing data models, storage solutions, and database structures for efficient data retrieval, analysis, and processing.
- Experience in implementing data quality, data lifecycle management, and ensuring data governance, security, and compliance across all layers of the data ecosystem.
- Evaluation and integration of external data sources, services, and vendor tooling when needed.
Benefits & Perks
100% remote work
Monthly WFH allowance
Career development program with 360º feedback
Time allocated during the week for tech training (online courses, conferences, books, etc.)
Mentoring program (opportunities to be a mentor or mentee)
Access to Zartis Wellbeing Hub Kara Connect with sessions with specialists (mental health, nutrition, physiotherapy, fitness)
Participation in multicultural work environment with online team-building events, webinars, parties, and contests
Ready to Apply?
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