‍- Implementing a data engineering pipeline that incorporates various data sources such as databases, APIs, and JSON/XML/CSV files. Ensuring the data pipeline is efficient, scalable, and reliable.
- Writing code and scripts to automate data extraction, transformation processes (if needed).
- Monitoring and optimizing the performance of the data engineering pipeline.
- Documenting the pipeline structure, workflows, and configurations.

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Team:‍

- The candidate will be part of Data Enablement Team, directly reporting to Technical Team Lead and to Product Owner.
- The candidate will collaborate with DE Testing Team and occasionally may support extended Mantis Ecosystem teams, products, and value streams.
- Proactivity in tasks assigned, troubleshooting and active communication within and beyond the team is a must.

Required: ‍
- Intermediate knowledge of Python, YAML, SQL, and CI/CD (Continuous Integration / Continuous Deployment) practices.
- Knowledge of version control systems (Git).
- Familiarity with data engineering concepts and techniques.
- Proficiency in working with various data formats (JSON, XML, and CSV).
- Proficiency in English, both written and spoken (level C1).

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Preferred/Nice to have:
- Familiarity with Apache Airflow, AWS technologies - AWS Glue, S3.
- Experience with building and maintaining data pipelines.
- Understanding of cloud-based data processing and storage concepts.
- Experience with agile development methodologies.
- Experience with virtualization and real-time / near real-time ingest concepts.

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Expectations:
- The candidate is expected to comply with all applicable Company´s policies and
procedures, adhere to Good Documentation Practices and follow recommendations from Team Leads.
- Collaborate with cross-functional teams to gather requirements and understand data integration needs.
- Write clean and maintainable code and configuration for data processing and automation.
- Demonstrate strong problem-solving skills and the ability to troubleshoot and resolve issues.
- Stay updated with the latest trends and advancements in data engineering.
- Work independently on data engineering pipelines.
- Continuously improve and optimize the data engineering processes.
- Communicate effectively with team members

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