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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, containerization, and best practices in data engineering.
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 ensuring 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 on 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 and 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.
- Implementation of data quality, data lifecycle management, and data governance practices across all layers of the data ecosystem.
- Evaluation and integration of external data sources, services, and vendor tooling as needed.
- Stay up to date with emerging trends in data engineering, workflow orchestration, and MLOps-adjacent tooling such as Airflow, Dagster, MLflow, HEAT.
Benefits & Perks
100 Remote Work
WFH allowance Monthly payment as financial support for remote working
Career Growth through a career development program with 360º feedback
Time allocated during the week for tech training, including online courses, English classes, books, conferences, and events
Mentoring Program for both mentoring and receiving mentorship
Access to Zartis Wellbeing Hub Kara Connect with sessions with mental health professionals, nutritionists, physiotherapists, fitness coaches, and webinars
Multicultural working environment with online team-building games, webinars, parties, and activities
Ready to Apply?
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