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India
Remote only
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data & Platform Engineer based in India.
As a Data & Platform Engineer, you will contribute to the development and enhancement of a central data platform as part of a large-scale IT transformation. You will design, build, and optimize scalable data pipelines and data products using modern engineering technologies. Working with Python, SQL, dbt, Apache Spark, and Apache Airflow, you will help create efficient and reliable data workflows. You will collaborate closely with data scientists, business stakeholders, and architecture teams to deliver high-quality data solutions. Your work will support the evolution of a modular, reusable, and future-ready platform architecture. This role offers the opportunity to contribute to strategic technology initiatives within a collaborative and multidisciplinary environment.
You will help develop, operate, and continuously improve a modern data platform, ensuring its scalability, reliability, and performance while supporting the organization's broader technology transformation. Your responsibilities will include:
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 platform components, ensuring scalability, stability, and reusability.
Collaborate with data scientists, subject matter experts, and architecture teams to develop and deliver high-quality data products.
Ensure data quality, performance, consistency, and governance across the platform.
Contribute to the continuous evolution of platform architecture 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, platform reliability, and maintainability.
Contribute to the development of a modern, modular, and future-proof data ecosystem.
The ideal candidate is an experienced Data Engineer with a strong interest in Platform Engineering and a solid understanding of modern data architectures. You should combine technical expertise with analytical thinking, independent problem-solving abilities, and excellent collaboration skills. The following qualifications are expected:
Several years of professional experience as a Data Engineer, ideally with a strong focus on Platform Engineering.
Solid technical knowledge of Python, SQL, Apache Spark, dbt, and Apache Airflow.
Experience designing, building, and operating modern data platforms and data architectures.
Good understanding of data modeling, data engineering best practices, and scalable data processing solutions.
Familiarity with DevOps principles and automated deployment processes.
Structured, analytical, and independent working style, with a strong solution-oriented mindset.
Excellent communication and interpersonal skills, with the ability to collaborate effectively across multidisciplinary teams.
Fluent German and English communication skills.
Additional qualifications that would be an advantage:
Experience with Kubernetes, Starburst, or Trino.
Background in building and operating enterprise-level data platforms.
Knowledge of data science and analytics platforms.
Experience working in regulated environments or data-intensive organizations.
Understanding of cloud-native architectures and modern platform engineering concepts.
Remote Work: Worldwide remote working opportunity.
Contract Type: Fixed-term employment contract.
Start Date: As soon as possible.
Professional Development: Opportunity to work with modern data engineering and platform technologies, including Python, Spark, dbt, and Airflow.
Strategic Projects: Involvement in a comprehensive IT transformation and the development of a central data platform.
Technical Growth: Exposure to scalable platform architectures, automation, DevOps, and modern data technologies.
Collaborative Environment: Work alongside data scientists, business experts, engineers, and architecture specialists.
