Data & Platform Engineer
New
J
JobgetherData engineering
Based in IndiaContractMiddle
Salary not disclosed
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Job Details
- Languages
- Fluent German and English communication skills.
- Experience
- Several years of professional experience as a Data Engineer
- Required Skills
- PythonSQLApache AirflowData engineeringSparkDevOpsData modelingdbt
Requirements
- Have several years of professional experience as a Data Engineer, ideally with a strong focus on Platform Engineering.
- Have solid technical knowledge of Python, SQL, Apache Spark, dbt, and Apache Airflow.
- Have experience designing, building, and operating modern data platforms and data architectures.
- Understand data modeling, data engineering best practices, and scalable data processing solutions.
- Be familiar with DevOps principles and automated deployment processes.
- Communicate fluently in German and English.
- Kubernetes, Starburst, or Trino experience is an advantage.
- Enterprise-level data platform experience is an advantage.
- Knowledge of data science and analytics platforms is an advantage.
- Experience in regulated environments or data-intensive organizations is an advantage.
- Understanding of cloud-native architectures and modern platform engineering concepts is an advantage.
Responsibilities
- Develop, operate, and optimize data pipelines for modern data and analytics platforms.
- Design, implement, and enhance ETL/ELT processes using SQL, Python, dbt, and Apache Spark.
- Build, automate, and monitor data workflows using Apache Airflow.
- Support the design, implementation, and operation of scalable, stable, and reusable platform components.
- Collaborate with data scientists, subject matter experts, and architecture teams to deliver data products.
- Ensure data quality, performance, consistency, and governance across the platform.
- Contribute to platform architecture evolution and the integration of new technologies and services.
- Support automation and DevOps initiatives within the data platform environment.
- Apply data engineering best practices to improve processing efficiency, reliability, and maintainability.
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